CrawlJobs Logo

Senior AI Hardware Architect

United States, Mountain View 119800.00 - 234700.00 USD / Year · Job Posted February 13, 2026
Apply Position
Job Link Share

Job Description

Join the Systems Planning and Architecture (SPARC) team within Microsoft’s Azure Hardware Systems and Infrastructure (AHSI) organization, the team behind Microsoft’s expanding Cloud Infrastructure and for powering Microsoft’s “Intelligent Cloud” mission. Microsoft delivers more than 200 online services to more than one billion individuals worldwide, and AHSI is the team behind our expanding cloud infrastructure. We deliver the core infrastructure and foundational technologies for Microsoft's cloud businesses including Microsoft Azure, Bing, MSN, Office 365, OneDrive, Skype, Teams and Xbox Live. We are seeking a Senior AI Hardware Architect to join the AI Systems Architecture (ASA) group, where we define, analyze, and optimize next-generation AI accelerator platforms and large-scale inference and training systems. In this role, you will lead performance analysis, profiling, kernel-level optimization, and end-to-end performance characterization across GPU and accelerator architectures, working across hardware, software, and system boundaries. You will analyze real-world AI workloads across modern GPU platforms and in-house AI accelerators, identifying performance bottlenecks and architectural trade-offs through rigorous measurement and benchmarking. A key aspect of this role is correlating on-silicon measurements, software traces, and kernel execution behavior with architectural models and simulators, enabling deep insight into performance behavior and guiding data-driven architectural decisions. You will collaborate closely with architecture, microarchitecture, compiler, runtime, and systems teams, and contribute to the development of data correlation, analysis, and visualization tools that improve performance insight and optimization velocity. Through quantitative analysis and cross-platform understanding, you will play a critical role in shaping future accelerator and system architectures across the AI hardware and software stack.

Job Responsibility

  • Lead performance analysis, profiling, and benchmarking across GPU and in-house AI accelerator architectures, applying rigorous data and statistical analysis to identify complex performance bottlenecks, root causes, and optimization opportunities across hardware, software, and system layers
  • Run and analyze end-to-end AI models on production-like serving infrastructure, performing deep dives into modern AI serving stacks (e.g., optimized LLM serving frameworks, schedulers, runtimes, and memory management systems) to understand performance behavior, scalability limits, and system-level trade-offs
  • Provide data-driven recommendations and architectural trade-offs to senior technical leadership, balancing performance, complexity, cost, quality, reliability, and development timelines to inform accelerator and system architecture decisions
  • Develop and implement technical solutions to complex performance, quality, and design challenges, including kernel-level optimization, architectural tuning, and system-level performance improvements across multiple products or feature areas
  • Correlate on-silicon measurements, software traces, and kernel execution behavior with architectural models and simulators, ensuring alignment between measured performance and architectural intent, and identifying gaps that drive future design enhancements
  • Design, build, and evolve data correlation, analysis, and visualization tools and workflows that scale performance insight, accelerate debugging, and improve clarity and communication of optimization opportunities across teams
  • Lead and contribute to design and performance documentation, including architecture reviews, performance reports, functional specifications, and customized analyses
  • communicate progress, risks, and recommendations within and across teams, and help identify and mitigate significant project risks

Requirements

  • Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience OR equivalent experience
  • Ability to meet Microsoft, customer, and/or government security screening requirements for this role
  • Passing the Microsoft Cloud background check upon hire/transfer and every two years thereafter

Nice to have

  • Doctorate in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 6+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR equivalent experience
  • MS or PhD in Machine Learning, Computer Architecture/Systems, Electrical Engineering, High-Performance Computing, or related areas
  • 4+ years of experience in Computer Architecture, AI Systems, or closely related technical domains
  • Experience with GPU and AI accelerator architectures, including compute pipelines, memory hierarchies, interconnects, and parallel execution models
  • Demonstrated expertise in performance profiling, benchmarking, and root-cause analysis, using hardware performance counters, software traces, and workload-level measurements
  • Hands-on experience with kernel-level performance analysis and optimization, and correlating kernel behavior with architectural and system-level performance
  • Strong programming and scripting skills in Python and C/C++ for performance analysis, tooling, benchmarking, and automation
  • Experience with architectural modeling or simulators and correlating modeled behavior with measured hardware performance
  • Experience running and analyzing end-to-end AI models on serving or training infrastructure, with the ability to diagnose performance issues across hardware, runtime, and system layers
  • Hands-on experience with AI frameworks and runtimes, including PyTorch, and familiarity with modern AI serving stacks such as vLLM and SGLang frameworks
  • Ability to communicate complex technical concepts clearly through design documentation, performance reports, functional specifications, and technical presentations

Looking for more opportunities?

Search for other job offers that match your skills and interests.

Similar Jobs for

Senior AI Hardware Architect

8 matching positions

Senior Product Architect – AI Data Center & SONiC Networking

Senior Product Architect – AI Data Center & SONiC Networking. This role has been...
Location
Location
United States , San Jose
Salary
Salary:
172000.00 - 349000.00 USD / Year
https://www.hpe.com/ Logo
Hewlett Packard Enterprise
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 10 plus years of experience in data center networking, AI infrastructure, or high-performance systems
  • Deep expertise in: SONiC architecture and internals
  • Large-scale Ethernet fabrics
  • High-speed SerDes (112G/224G PAM4) and their impact on system performance
  • Strong understanding of ASIC pipelines, buffering, ECMP behavior, and congestion mechanisms
  • Proven ability to diagnose cross-layer performance and reliability issues involving software, hardware, and physical-layer interactions
  • Hands-on experience with RDMA/RoCE, congestion control, and lossless Ethernet at scale
  • Experience with automation and tooling (Python, Ansible, Terraform) in large-scale environments
  • Industry certifications (e.g., CCIE, JNCIE, NVIDIA) or equivalent practical experience preferred
Job Responsibility
Job Responsibility
  • Architect ultra-low-latency, lossless Ethernet fabrics supporting tens of thousands of GPUs for AI training and inference
  • Own the end-to-end SONiC platform architecture and fabric strategy, spanning control plane, management plane, data-plane integration, and operations at scale
  • Define multi-generation fabric and platform strategy across switch ASICs, NICs, SerDes capabilities, cabling, and system constraints, aligned to power, performance, and deployment realities
  • Own link-level and physical-layer requirements as they impact SONiC performance, including high-speed PAM4 signaling (112G/224G), error handling, and hardware/software interaction
  • Align SONiC architectures with next-generation GPU, NIC, and switch platforms, ensuring optimal performance across hardware and software boundaries
  • Define SONiC capabilities for AI and HPC workloads, including: Lossless Ethernet and RoCE
  • Congestion management, QoS, and ECN
  • Dynamic and flow-based load balancing
  • Drive scale, performance, and resiliency targets for SONiC-based fabrics, including fast convergence, hitless upgrades, and failure recovery
  • Define and enforce system-level validation criteria, including scale testing, fault injection, performance benchmarking, and upgrade scenarios
What we offer
What we offer
  • Health & Wellbeing
  • Personal & Professional Development
  • Unconditional Inclusion
  • Fulltime
Read More
Arrow Right

Senior AI Network Architect

Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) is the...
Location
Location
United States , Redmond
Salary
Salary:
119800.00 - 234700.00 USD / Year
https://www.microsoft.com/ Logo
Microsoft Corporation
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience
  • OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience
  • OR equivalent experience
  • Ability to meet Microsoft, customer and/or government security screening requirements
  • Microsoft Cloud Background Check
  • 3+ years of experience in designing AI backend networks and integrating them into large-scale GPU systems
  • Proven expertise in system architecture across compute, networking, and accelerator domains
  • Deep understanding of RDMA protocols (RoCE, InfiniBand), congestion control (DCQCN), and Layer 2/3 routing
  • Experience with optical interconnects (e.g., PSM, WDM), link budget analysis, and transceiver integration
  • Familiarity with signal integrity modeling, link training, and physical layer optimization
Job Responsibility
Job Responsibility
  • Spearhead architectural definition and innovation for next-generation GPU and AI accelerator platforms, with a focus on ultra-high bandwidth, low-latency backend networks
  • Drive system-level integration across compute, storage, and interconnect domains to support scalable AI training workloads
  • Partner with silicon, firmware, and datacenter engineering teams to co-design infrastructure that meets performance, reliability, and deployment goals
  • Influence platform decisions across rack, chassis, and pod-level implementations
  • Cultivate deep technical relationships with silicon vendors, optics suppliers, and switch fabric providers to co-develop differentiated solutions
  • Represent Microsoft in joint architecture forums and technical workshops
  • Evaluate and articulate tradeoffs across electrical, mechanical, thermal, and signal integrity domains
  • Frame decisions in terms of TCO, performance, scalability, and deployment risk
  • Lead design reviews and contribute to PRDs and system specifications
  • Shape the direction of hyperscale AI infrastructure by engaging with standards bodies (e.g., IEEE 802.3), influencing component roadmaps, and driving adoption of novel interconnect protocols and topologies
  • Fulltime
Read More
Arrow Right

Senior AI Software Architect

Do you want to be at the forefront of innovating the latest hardware designs to ...
Location
Location
United States , Redmond
Salary
Salary:
119800.00 - 234700.00 USD / Year
https://www.microsoft.com/ Logo
Microsoft Corporation
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
  • Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
  • These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter
Job Responsibility
Job Responsibility
  • Port and optimize large-scale AI models (e.g., foundation models, diffusion models, YOLO) to run efficiently on Maia hardware
  • Integrate models using frameworks such as PyTorch, ONNX, vLLM, and SGLang
  • Apply techniques like KV cache quantization (e.g., BF16 → FP8), checkpointing, and re-sharding for efficient inference and training
  • Experiment with parallelism strategies (TP, PP) and analyze performance impacts across interconnects (NVLink vs PCIe)
  • Collaborate on improving inference pipelines, including KV caching in sglang/vllm and performance tuning at the PyTorch level
  • Work with Triton kernels for basic operations (e.g., FP8 dequantization) and assist in kernel performance analysis
  • Partner with hardware architects and kernel developers for co-design discussions
  • Communicate effectively with multiple stakeholders to align on performance goals and deliverables
  • Fulltime
Read More
Arrow Right

Senior Principal AI Infrastructure Architect

The Senior Principal AI Infrastructure Architect is a highly skilled and advance...
Location
Location
Italy , Milano
Salary
Salary:
Not provided
nttdata.com Logo
NTT DATA
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • Significant experience in a consulting, presales or architecture role within a large-scale (preferably multi-national) technology services environment, with a track record of leading AI infrastructure pursuits
  • Demonstrable experience designing and delivering production AI platforms — from single multi-GPU servers through to multi-rack training clusters and inference factories
  • Strong working knowledge of the AI hardware vendor landscape (NVIDIA, AMD, Intel, Dell, HPE, Lenovo, Supermicro, Cisco, Pure, VAST, WEKA, DDN, NetApp) and how to position partner ecosystems competitively
  • Proven ability to translate AI workload requirements (model size, parameter count, sequence length, throughput SLOs, latency targets) into accurate hardware bills of materials and sizing justifications
  • Significant client engagement and consulting experience, including client needs assessment, change management and the ability to identify whitespace for follow-on AI infrastructure and managed-services work
  • Significant business development and presales experience on infrastructure-led deals, ideally including sovereign AI, AI Factory or regulated-industry GenAI programmes
  • Strong understanding of how AI infrastructure integrates with business processes, applications, data platforms and existing enterprise architecture
  • Bachelor's degree or equivalent in Information Technology, Engineering, Computer Science or a related field
  • Deep, hands-on knowledge of AI hardware: GPU and accelerator portfolios (NVIDIA Hopper / Blackwell, AMD MI300/MI325, Intel Gaudi 3, emerging custom silicon), host CPU platforms (Intel Xeon, AMD EPYC, NVIDIA Grace), system topologies (HGX, DGX, MGX, OAM) and how each choice maps to specific AI workloads
  • Strong understanding of AI-class storage: parallel filesystems, all-flash NVMe platforms, S3-class object stores, checkpoint and dataset pipelines and the I/O patterns of large-scale training and inference (VAST, WEKA, DDN EXAScaler, Pure FlashBlade, NetApp ONTAP AI, Dell PowerScale)
Job Responsibility
Job Responsibility
  • Lead the end-to-end design of large, complex AI infrastructure solutions — covering accelerated compute (NVIDIA H100/H200/B200 and GB200 NVL72, AMD Instinct MI300X/MI325X, Intel Gaudi 3), CPU host platforms (Intel Xeon, AMD EPYC, NVIDIA Grace), high-throughput storage tiers and lossless AI fabric — for enterprise, sovereign AI and AI Factory clients
  • Architect reference designs built on NVIDIA DGX/HGX SuperPOD, Dell AI Factory with NVIDIA, Cisco Nexus HyperFabric AI, HPE / Lenovo / Supermicro accelerated compute and equivalent platforms, balancing single-node performance with cluster-scale efficiency
  • Size and validate GPU clusters against real workloads — foundation-model pre-training, distributed fine-tuning, RAG, real-time and batch inference — using the right combination of NVLink/NVSwitch domains, InfiniBand NDR/XDR or Ultra Ethernet / NVIDIA Spectrum-X fabrics and tiered NVMe and parallel storage (VAST, WEKA, DDN, Pure FlashBlade, NetApp ONTAP AI, Dell PowerScale)
  • Define the supporting datacenter design: high-density power (50–140 kW/rack), direct-to-chip and rear-door liquid cooling, structured cabling for AI fabrics and modular deployment models across on-prem, colo and sovereign-cloud footprints
  • Work closely with the sales team to drive the presales process for AI infrastructure pursuits — client discovery, technical workshops, proposal writing, executive presentations and bid defence
  • Translate clients' AI ambitions and business outcomes into a hardware and platform roadmap, positioning NTT DATA's end-to-end portfolio — silicon, systems, storage, fabric, MLOps stack and managed services — to land service-led AI solutions
  • Lead integration of compute, storage, networking, the AI software stack (CUDA, ROCm, Triton, NIM, NVIDIA AI Enterprise, Run:ai, Slurm, Kubernetes / Kubeflow) and managed-service operating models across multiple domains, delivery units and geographies
  • Build business cases, TCO and unit-economics models (cost per token, cost per training run, GPU-hour economics) and end-to-end transition roadmaps for cloud-to-private AI migrations and sovereign AI deployments
  • Define architectural principles for AI infrastructure — accelerator utilisation, data gravity, multi-tenancy, model lifecycle, energy efficiency — and apply them to influence architectural outcomes and governance
  • Develop As-Is, Vision, FMO and To-Be AI platform architectures, identify gaps and develop transition roadmaps
  • Fulltime
Read More
Arrow Right

Senior AI Infrastructure Engineer - Training Platform

As a Software Engineer on the Machine Learning Infrastructure team, you will bui...
Location
Location
United States , San Francisco; Seattle; New York
Salary
Salary:
216000.00 - 270000.00 USD / Year
scale.com Logo
Scale
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 5+ years of experience in backend or infrastructure engineering, with at least 2 years focused on orchestrating ML workloads at scale (100+ GPU nodes)
  • Strong programming skills in one or more languages (e.g. Python, Go, Rust, C++)
  • Experience with complex compute management systems that cover queueing, quotas, preemption, and gang scheduling
  • Experience with distributed training infrastructure, such as EFA, Infiniband, and topology-aware scheduling
  • Experience with distributed storage systems (e.g. Lustre, S3) as they relate to training throughput
  • Expert-level knowledge of Kubernetes internals (Custom Resources, Operators, Admission Controllers) and how they interact with device plugins for specialized hardware
  • Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform)
  • Proven ability to solve complex problems and work independently in fast-moving environments
Job Responsibility
Job Responsibility
  • Architect and scale a multi-tenant orchestration layer that abstracts away the complexity of GPU clusters, ensuring high utilization and seamless job recovery
  • Design and implement scheduling primitives to optimize the lifecycle of training jobs
  • Develop deep observability and automated health-checking into the training stack to proactively identify and isolate hardware failures
  • Evaluate and integrate emerging technologies in the CNCF and AI ecosystem (e.g. Ray, Kueue), making data-driven build vs. buy decisions that balance velocity with long-term maintainability
  • Work closely with Finance and Procurement teams to drive our capacity planning process
  • Participate in our team's on call process to ensure the availability of our services
  • Own projects end-to-end, from requirements, scoping, design, to implementation, in a highly collaborative and cross-functional environment
What we offer
What we offer
  • Comprehensive health, dental and vision coverage
  • retirement benefits
  • a learning and development stipend
  • generous PTO
  • commuter stipend (may be eligible)
  • Fulltime
Read More
Arrow Right

Senior AI Presales Consultant

We are seeking a high-impact, strategic AI Presales Consultant to join our elite...
Location
Location
India , Mumbai
Salary
Salary:
Not provided
eviden.com Logo
Eviden
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 7+ years in a customer-facing technical role (e.g., Presales, Solutions Architecture, AI Specialist, or Technical Consulting), with a proven track record of designing large-scale AI, ML, or HPC solutions
  • Deep, hands-on understanding of LLM architectures. Must be able to architect, explain, and build PoCs for RAG pipelines, including vector databases (e.g., Milvus, Pinecone, Chroma), embedding models, and data ingestion strategies
  • Direct experience in sizing AI infrastructure. Must be able to perform "napkin math" and detailed calculations for GPU, CPU, memory, and network requirements
  • Must be able to fluently discuss performance metrics (tokens/second, latency, throughput, TFLOPS) and their relationship to hardware choice (e.g., NVIDIA H100 vs. A100, memory bandwidth, interconnects like NVLink/InfiniBand)
  • Expertise in the AI software stack. Strong understanding of MLOps principles (Kubeflow, MLflow), Kubernetes (K8s) for AI workloads, and model serving platforms (NVIDIA Triton, KServe, or similar)
  • Strong, current knowledge of the AI model landscape (e.g., Llama family, Mistral, GPT-family, foundation models). Ability to discuss fine-tuning techniques, quantization, and pruning
  • Exceptional communication, whiteboarding, and presentation skills. Ability to translate executive-level business needs into detailed technical architecture and build a compelling C-level value proposition
  • Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related engineering field
Job Responsibility
Job Responsibility
  • Strategic Client Advisory: Lead executive-level "Art of the Possible" workshops and technical discovery sessions to understand a client's business goals, data readiness, and AI maturity
  • Full-Stack Solution Architecture: Design holistic, end-to-end AI solutions that synergize our supercomputing hardware, AI software platform, and MLOps capabilities to meet specific client needs
  • Generative AI & LLM Expertise: Act as the subject matter expert on Generative AI. Architect and evangelize scalable data ingestion and preparation pipelines, specializing in Retrieval-Augmented Generation (RAG) frameworks
  • Infrastructure Sizing & Performance Modelling: Analyse customer workloads (data volume, model complexity, training frequency, inference throughput) to accurately size the required platform infrastructure, including Kubernetes clusters, data storage, and software licenses. This includes calculating compute, storage, and network requirements based on key performance metrics like model parameters, token performance (tokens/sec), desired latency, and concurrent user load
  • Model & Software Consultation: Advise clients on AI model selection, comparing the trade-offs of open-source vs. proprietary LLMs, fine-tuning vs. foundation models, and model quantization
  • Position and demonstrate our proprietary AI software platform, MLOps tools, and libraries, integrating them into the client's ecosystem
  • Inference Optimization: Design and architect robust, low-latency, and high-throughput inference solutions for complex AI models, including large-scale LLM serving
  • User Experience (UX) Advocacy: Collaborate with client teams to define the end-user experience, ensuring the solution delivers tangible business value and a seamless interface for data scientists, analysts, and application users
  • Sales Cycle Enablement: Own the technical narrative throughout the sales cycle. Build and deliver compelling presentations, custom demonstrations, and Proofs of Concept (PoCs). Lead the technical response to complex RFIs/RFPs
  • Fulltime
Read More
Arrow Right

Senior Systems Architect (IIOT & Cloud)

Location
Location
United Kingdom
Salary
Salary:
75000.00 GBP / Year
carbonthirteen.com Logo
Carbon13
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 7+ years of experience in systems architecture, on-site controllers, or IIoT platforms
  • In-depth experience in designing, testing, or installing control interfaces for at least one of these commercial or industrial asset categories: Battery BESS inverters, HVAC systems, heat pumps, and thermal storage utilising IIOT controllers, PLCs, RTUs, or Building Management Systems (BMS/BEMS)
  • Strong domain background in energy systems, industrial automation, HVAC automation or similar industries
  • Deep knowledge in any of the industrial communication protocols (Modbus, BACnet, OPCC, MQTT, OPC-UA,)
  • Strong understanding of networking and secure communications
  • Experience with IEC 61131-3 (industrial automation programming)
  • Proficiency in at least one of the programming languages: C, C++, Go, or Python
  • Strong experience with embedded Linux, real-time systems, and controller design
  • Experience designing cloud-native architectures (Azure)
  • Deep expertise in time-series data systems (e.g. Postgres, InfluxDB)
Job Responsibility
Job Responsibility
  • Lead the end-to-end design and evolution of our next-generation energy technology platform
  • Define and own the system architecture across edge devices and cloud, ensuring it is secure, scalable, standards-compliant, and future-proof
  • Translate complex energy system requirements into robust, production-ready solutions
  • Design architectures that reliably support tens of thousands of distributed industrial devices
  • Integrate real-time system within built environment and industrial set up with advanced connectivity (MQTT, OPC-UA / IEC 62541) and cloud-native data platforms
  • Develop Edge-AI control models and translate them into production systems across both edge and cloud environments
  • Set architectural direction while actively guiding firmware, hardware, and cloud implementation across teams
  • Prioritise secure, standards-compliant, and future-proof systems that can scale, adapt, and operate globally over time
What we offer
What we offer
  • Remote-first with occasional travel to London/Cambridge for team meetings
  • Travel to customer or manufacturer locations within the UK, EU, or internationally
  • Co-working support – Eagle Labs membership for Cambridge-based hires (other locations TBD)
  • Competitive salary + share options – negotiable depending on cash vs equity preference
  • As a founding member, you will have influence in shaping future benefits + leave
  • Fulltime
Read More
Arrow Right

Senior Distinguished AI Engineer

At Capital One, we are creating responsible and reliable AI systems, changing ba...
Location
Location
United States , San Francisco; Richmond; San Jose; Cambridge; McLean; New York
Salary
Salary:
286200.00 - 392000.00 USD / Year
capitalone.com Logo
Capital One
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 10 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies
  • At least 10 years of experience programming with Python, Go, Scala, or Java
  • 9 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
  • Experience architecting, designing, developing, integrating, delivering, and supporting complex enterprise AI systems
  • Demonstrated ability to lead and mentor an engineering organization and influence cross-functional stakeholders up to the SVP level
  • Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang
  • Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
  • Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production
  • Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
Job Responsibility
Job Responsibility
  • Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
  • Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability
  • Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more
  • Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems
  • Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One
What we offer
What we offer
  • performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
  • comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
  • Fulltime
Read More
Arrow Right