Machine Learning Hardware Architect Job at Meta (Sunnyvale)

Machine Learning Performance Modeling Architect

Reality Labs focuses on delivering Meta's vision through On-device AI. The compu...

Location

United States , Sunnyvale

Salary:

178000.00 - 250000.00 USD / Year

Search Machine Learning Research Engineer

Perplexity is seeking an experienced Senior Machine Learning Engineer to help bu...

Location

Germany , Berlin

Salary:

Not provided

Perplexity

Expiration Date

Until further notice

Requirements

Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
Proven track record with large-scale search or recommender systems
Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models
Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications
Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)
Self-driven, with a strong sense of ownership and execution
Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

Job Responsibility

Relentlessly push search quality forward — through models, data, tools, or any other leverage available
Architect and build core components of the search platform and model stack
Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models
Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval
Deploy models — from boosting algorithms to LLMs — in a scalable and performant way
Build and optimize RAG pipelines for grounding and answer generation
Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery

Fulltime

Principal Machine Learning Engineer

With Prisma AIRS, Palo Alto Networks is building the world's most comprehensive ...

Location

United States , Santa Clara

Salary:

185200.00 - 299475.00 USD / Year

Palo Alto Networks

Expiration Date

Until further notice

Requirements

BS/MS or Ph.D. in Computer Science, a related technical field, or equivalent practical experience
Extensive professional experience in software engineering with a deep focus on MLOps, ML systems, or productionizing machine learning models at scale
Expert-level programming skills in Python are required
Deep, hands-on experience designing and building large-scale distributed systems on a major cloud platform (GCP, AWS, Azure, or OCI)
Proven track record of leading the architecture of complex ML systems and MLOps pipelines using technologies like Kubernetes and Docker
Mastery of ML frameworks (TensorFlow, PyTorch) and extensive experience with advanced inference optimization tools (ONNX, TensorRT)
A strong understanding of popular model architectures (e.g., Transformers, CNNs, GNNs) is a must
Demonstrated expertise with modern LLM inference engines (e.g., vLLM, SGLang, TensorRT-LLM) is required

Job Responsibility

Lead the architectural design of a highly scalable, low-latency, and resilient ML inference platform capable of serving a diverse range of models for real-time security applications
Define technical approaches to less-defined product requirements, ensuring the best fit between product features and technical implementation
Explore new product opportunities by maintaining a deep understanding of LLM and Generative AI research trends
Provide technical leadership and mentorship to the team, driving best practices in MLOps, software engineering, and system design
Drive the strategy for model and system performance, guiding research and implementation of advanced optimization techniques like custom kernels, hardware acceleration, and novel serving frameworks
Establish and enforce engineering standards for automated model deployment, robust monitoring, and operational excellence for all production ML systems
Act as a key technical liaison to other principal engineers, architects, and product leaders to shape the future of the Prisma AIRS platform and ensure end-to-end system cohesion
Tackle the most ambiguous and challenging technical problems in large-scale inference, from mitigating novel security threats to achieving unprecedented performance goals

What we offer

restricted stock units
bonus
employee benefits

Fulltime

Lead Machine Learning Engineer

Machine Learning Engineers specializing in Inference Optimization focus on maxim...

Location

Singapore , Singapore

Salary:

Not provided

Thoughtworks

Expiration Date

Until further notice

Requirements

Deep practical expertise in model and runtime optimization techniques (quantization, pruning, distillation, batching, caching)
Proven experience optimizing inference workloads using frameworks such as vLLM, NVIDIA Triton/Dynamo
Strong proficiency in deep learning frameworks (e.g. PyTorch, TensorFlow) with production deployment experience
Ability to diagnose and optimize performance using profiling tools (e.g. Nsight, PyTorch/TensorFlow profilers)
Solid understanding of GPU and accelerator architectures, and experience tuning workloads for cost and performance efficiency
Experience designing and benchmarking scalable inference systems across heterogeneous environments (GPU clusters, serverless, edge)
Familiarity with observability stacks, telemetry, and cost instrumentation for AI workloads
Demonstrated ability to lead small-to-medium engineering teams or technical workstreams
Skilled at balancing hands-on delivery with architectural oversight and mentorship
Strong communication and stakeholder engagement skills and are able to connect low-level optimizations with business impact

Job Responsibility

Lead the design and implementation of advanced model optimization pipelines, including quantization, pruning, and distillation
Architect and tune inference runtimes and serving frameworks to achieve optimal performance across deployments
Guide teams in implementing high-throughput serving strategies (continuous batching, KV caching, speculative decoding, asynchronous scheduling)
Develop benchmarks and performance dashboards to measure and communicate system-level efficiency improvements (throughput, latency, GPU utilization, cost)
Evaluate trade-offs across accuracy, performance, and cost, and design architectures to meet target SLAs across varied hardware environments (cloud, on-prem, edge)
Collaborate with infrastructure, MLOps, and product teams to embed inference optimization into production workflows and platform designs
Provide technical leadership and mentorship to engineers, fostering a culture of experimentation, rigor, and continuous performance improvement
Contribute to the development of internal frameworks, reference architectures, and playbooks for scalable and cost-efficient inference
Engage with clients to translate optimization outcomes into business value and articulate the ROI of technical improvements

What we offer

Learning & Development
Interactive tools
Numerous development programs
Teammates who want to help you grow
Empowering our employees in their career journeys

Fulltime

New

Applied Scientist

Amazon Devices is an inventive research and development company that designs and...

Location

United Kingdom , Cambridge

Salary:

Not provided

Amazon

Expiration Date

Until further notice

Requirements

Master's degree, or a PhD and experience in CS, CE, ML or related field
Experience programming in Java, C++, Python or related language
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

Job Responsibility

Apply and extend compression recipes (knowledge distillation, structured pruning, and post-training and quantization-aware quantization including low-bit and mixed-precision) to assigned models, achieving 20x to 100x compression while preserving model quality
Design and run healing recipes (fine-tuning and distillation that recover accuracy lost to compression), iterating on data mixes, objectives, and training settings until the compressed model meets its quality bar
Track emerging model architectures and dissect how they work internally, so you can choose where to compress, anticipate where accuracy will break, and design recovery strategies grounded in the model's actual structure
Build a library of compression-ready model entries: reference implementations, compression recipes, model cards, and benchmark results that partner teams can run self-service to produce deployment-ready artifacts for edge and cloud targets
Define the datasets, benchmarks, and KPIs that matter for your models, and build evaluation methodology that makes accuracy, latency, memory, and cost trade-offs explicit
Run fast feasibility gates on new model families and modalities before committing to long efforts, and pivot early when a candidate does not clear the bar
Capture platform friction as high-signal feedback: minimal reproductions and tracked fix requests that help platform and compression-science partners root-cause issues, so partner teams never rediscover the same blockers
Write reproducible, testable, well-documented code that meets the SDE I bar, so your recipes and results can be reproduced and built on by others
Collaborate with Applied Scientists, platform and compiler engineers, hardware architects, and partner teams
mentor interns and help newer teammates ramp up

Fulltime

Staff Software Engineer – Secondary Driving System

At General Motors, our Embodied AI teams are redefining what’s possible in drive...

Location

United States , Sunnyvale

Salary:

218800.00 - 335300.00 USD / Year

General Motors

Expiration Date

Until further notice

Requirements

BS, MS, or PhD in Computer Science, Robotics, Electrical/Mechanical Engineering, or a related field
or equivalent practical experience
8+ years of professional software engineering experience building production systems in robotics, autonomous vehicles, or other complex real‑time/control systems, including significant experience in perception and/or prediction
Strong proficiency in modern C++ (e.g., C++14/17 or later) in large, multi‑contributor codebases
experience using Python for tooling, data analysis, and ML experimentation
Demonstrated experience leading technical design and delivery of perception, tracking, or prediction systems in real‑time environments, including: Multi‑sensor fusion across camera, radar, and/or lidar (e.g., object‑level fusion, occupancy/freespace fusion, early/late fusion architectures)
Classical computer vision and geometric algorithms (feature extraction, multi‑view geometry, stereo, SfM, SLAM/visual odometry)
Multi‑object tracking (Kalman/extended/unscented filters, track‑to‑track fusion, track lifecycle management)
Motion prediction for road users (analytical kinematic models, maneuver‑based prediction, or learned trajectory forecasting models)
Proven track record of delivering reliable, high‑quality robotics or autonomous driving software to production, including: Testing strategies (simulation, HIL, scenario‑based testing, regression suites)

Job Responsibility

Serve as a technical lead for SDS software across multiple components of the stack, setting direction for algorithms, architectures, and system interfaces across features and releases
Own the end‑to‑end technical strategy for key SDS behaviors and features, spanning perception/prediction integration, planning, controls, and system‑level interactions
Balance hands‑on technical work with cross‑team leadership: you will still design and implement critical components in modern C++, while also guiding other senior and mid‑level engineers to deliver at scale
Collaborate closely with experts in perception, tracking, prediction, state estimation, localization, mapping, planning, controls, systems engineering, and safety to deliver robust, fail‑operational behaviors for Super Cruise and future products
Define technical vision & architecture
Set the technical direction for SDS software components with a focus on correctness, robustness, and predictable runtime behavior under tight latency and compute budgets
Architect scalable, modular multi‑sensor perception pipelines for camera, radar, and lidar, including detection, classification, lane/road feature extraction, freespace/occupancy, and environmental context
Establish and evolve interfaces and contracts between perception/prediction and upstream/downstream components (state estimation, localization, mapping, planning, controls, autonomy management)
Lead high‑impact projects
Lead design and delivery of multi‑object tracking systems (e.g., Kalman/extended/unscented filters, IMM, probabilistic data association, track lifecycle management) that provide stable, high‑quality tracks under real‑world noise and edge cases

What we offer

medical
dental
vision
Health Savings Account
Flexible Spending Accounts
retirement savings plan
sickness and accident benefits
life insurance
paid vacation & holidays
tuition assistance programs

Fulltime

Member of Technical Staff, Microsoft Robotics (Software Systems)

Microsoft's Discovery and Quantum (MDQ) division develops and delivers advanced ...

Location

United States , Redmond

Salary:

142800.00 - 274800.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science or related technical field AND 6+ 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
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

Architect and implement core platform components, including robotics SDKs, cloud-hosted Application Programming Interfaces (APIs), edge runtimes, and agent orchestration frameworks that enable developers and partners to compose interoperable autonomy capabilities (perception, planning, control, multi-agent coordination) into deployable mission workflows
Design the platform's extensibility and integration architecture, defining how first-party autonomy capabilities, first- and third-party models, partner hardware systems, and customer-specific logic are composed, versioned, tested, and deployed across cloud and edge environments
Build production-grade data infrastructure spanning the full robotics lifecycle including instrumentation libraries, data acquisition services, human-in-the-loop workflows, dataset versioning and curation pipelines, and data quality governance supporting both real-world and synthetic/simulated data at scale
Own cross-cutting platform concerns including authentication and authorization across cloud-edge boundaries, API versioning and backward compatibility, multi-tenant isolation, and performance at the latencies required by real-time robotic control loops
Drive the developer experience for the Microsoft Robotics platform, to include defining the Command Line Interface (CLI), SDK patterns, documentation strategy, sample code, and inner-loop development workflow that make it fast and reliable for internal teams and external partners to build on the platform
Collaborate with autonomy, simulation, and evaluation teams to ensure that platform primitives (compute orchestration, data routing, model serving, experiment tracking) meet the performance, reliability, and reproducibility requirements of Machine Learning (ML) training, sim-to-real transfer, and online evaluation workloads
Lead technical design reviews, write architecture decision records, and establish engineering practices for the platform team, mentoring senior engineers and raising the bar for code quality, testing, and operational readiness across the organization.

What we offer

Benefits and other compensation may be eligible
Find additional benefits and pay information at the provided link.

Fulltime

Ai Ml Engineer

AI/ML Engineer | Mangalore / Hybrid India | Full-Time. About the Opportunity: Ab...

Location

India , Mangalore

Salary:

Not provided

Abotts

Expiration Date

Until further notice

Requirements

BS/MS/PhD in Computer Science, Engineering, or a related field
2+ years of experience designing and deploying AI/GenAI solutions in production environments
Proficiency in Python
hands-on experience with TensorFlow, PyTorch, and NLP frameworks
Demonstrated experience training large language models and managing compute infrastructure
Familiarity with NoSQL and Vector databases
Working knowledge of DevOps practices: Git, CI/CD, Linux scripting
Experience with GenAI evaluation and testing frameworks
Prior experience leading or mentoring engineers

Job Responsibility

Architect and execute the roadmap for AI, GenAI, and Agentic AI capabilities
Lead and mentor a small engineering team — translating vision into actionable plans with clear ownership
Deliver rapid, high-quality implementations in a fast-paced, dynamic startup environment
Collaborate cross-functionally with hardware and product teams to accelerate go-to-market timelines
Contribute to the company's thought leadership through white papers and patent filings

What we offer

Competitive compensation and may be meaningful early-stage equity (ESOPs)
The chance to shape AI strategy at a company redefining an entire industry
High-trust, high-accountability culture that rewards initiative
Flexible hybrid work environment
Full company details shared at first conversation stage

Fulltime

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Machine Learning Hardware Architect

Job Description

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