CrawlJobs Logo

Senior Research Engineer - Inference ML

United States; Canada, Sunnyvale · Job Posted February 17, 2026
Apply Position
Job Link Share

Job Description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Job Responsibility

  • Design, implement, and optimize state-of-the-art transformer architectures for NLP and computer vision on Cerebras hardware
  • Research and prototype novel inference algorithms and model architectures that exploit the unique capabilities of Cerebras hardware, with emphasis on speculative decoding, pruning/compression, sparse attention, and sparsity
  • Train models to convergence, perform hyperparameter sweeps, and analyze results to inform next steps
  • Bring up new models on the Cerebras system, validate functional correctness, and troubleshoot any integration issues
  • Profile and optimize model code using Cerebras tools to maximize throughput and minimize latency
  • Develop diagnostic tooling or scripts to surface performance bottlenecks and guide optimization strategies for inference workloads
  • Collaborate across teams, including software, hardware, and product, to drive projects from inception through delivery

Requirements

  • Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related technical field AND 7+ years of ML software development experience
  • OR Master’s degree in Computer Science or related technical field AND 4+ years of software development experience
  • OR PhD in Computer Science or related technical field with 2+ years of relevant research or industry experience
  • OR Equivalent practical experience
  • 4+ years of experience testing, maintaining, or launching software products, including 2+ years of experience with software design and architecture
  • 3+ years of experience in software development focused on machine learning (e.g., deep learning, large language models, or computer vision)
  • Strong programming skills in Python and/or C++
  • Experience with Generative AI and Machine Learning systems
  • Evidence of research impact in machine learning, such as publications at top conferences (NeurIPS, ICLR, ICML, ACL, EMNLP, MLSys) or comparable contributions to widely used open-source projects or high-quality preprints

Nice to have

  • Master’s degree or PhD in Computer Science, Computer Engineering, or a related technical field
  • Experience independently driving complex ML or inference projects from prototype to production-quality implementations
  • Hands-on experience with relevant ML frameworks such as PyTorch, Transformers, vLLM, or SGLang
  • Experience with large language models, mixture-of-experts models, multimodal learning, or AI agents
  • Experience with speculative decoding, neural network pruning and compression, sparse attention, quantization, sparsity, post-training techniques, and inference-focused evaluations
  • Familiarity with large-scale model training and deployment, including performance and cost trade-offs in production systems
  • Triton/CUDA experience is a big plus

What we offer

  • Build a breakthrough AI platform beyond the constraints of the GPU
  • Publish and open source their cutting-edge AI research
  • Work on one of the fastest AI supercomputers in the world
  • Enjoy job stability with startup vitality
  • Our simple, non-corporate work culture that respects individual beliefs

Looking for more opportunities?

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

Similar Jobs for

Senior Research Engineer - Inference ML

8 matching positions

Senior ML Infrastructure Engineer, Inference Platform

About the Team: The ML Inference Platform is part of the AV ML Infrastructure or...
Location
Location
United States , Austin, Texas; Mountain View, California; Sunnyvale, California
Salary
Salary:
155420.00 USD / Year
gm.com Logo
General Motors
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 5+ years of industry experience, with focus on machine learning systems or high performance backend services
  • Expertise in either Python, C++ or other relevant coding languages
  • Expertise in ML inference, model serving frameworks (triton, rayserve, vLLM etc)
  • Strong communication skills and a proven ability to drive cross-functional initiatives
  • Ability to thrive in a dynamic, multi-tasking environment with ever-evolving priorities
Job Responsibility
Job Responsibility
  • Design and implement core platform backend software components
  • Collaborate with ML engineers and researchers to understand critical workflows, parse them to platform requirements, and deliver incremental value
  • Lead technical decision-making on model serving strategies, orchestration, caching, model versioning, and auto-scaling mechanisms for highly optimized use of accelerators
  • Drive the development of monitoring, observability, and metrics to ensure reliability, performance, and resource optimization of inference services
  • Proactively research and integrate state-of-the-art model serving frameworks, hardware accelerators, and distributed computing techniques
  • Lead technical initiatives across GM’s ML ecosystem
  • Raise the engineering bar through technical leadership, establishing best practices
  • Contribute to open source projects
  • represent GM in relevant communities
What we offer
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
Read More
Arrow Right

Senior ML Infrastructure / ML DevOps Engineer

We are looking for a Senior ML Infrastructure / DevOps Engineer who loves Linux,...
Location
Location
Salary
Salary:
Not provided
Pathway
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • Former or current Linux / systems / network administrator comfortable living in the shell and debugging at OS and network layers (systemd, filesystems, iptables/security groups, DNS, TLS, routing)
  • 5+ years of experience in DevOps/SRE/Platform/Infrastructure roles running production systems, ideally with high‑performance or ML workloads
  • Deep familiarity with Linux as a daily driver, including shell scripting and configuration of clusters and services
  • Strong experience with workload management, containerization, and orchestration (Slurm, Docker, Kubernetes) in production environments
  • Solid understanding of CI/CD tools and workflows (GitHub Actions, GitLab CI, Jenkins, etc.), including building pipelines from scratch
  • Hands-on cloud infrastructure experience (AWS, GCP, Azure), especially around GPU instances, VPC/networking, storage, and managed ML services (e.g., SageMaker HyperPod, Vertex AI)
  • Proficiency with infrastructure as code (Terraform, CloudFormation, or similar) and a bias toward automation over manual operations
  • Experience with monitoring and logging stacks (Grafana, Prometheus, Loki, CloudWatch, or equivalents)
  • Familiarity with ML pipeline and experiment orchestration tools (MLflow, Kubeflow, Airflow, Metaflow, etc.) and with model/version management
  • Solid programming skills in Python, plus the ability to read and debug code that uses common ML libraries (PyTorch, TensorFlow) even if you are not a full‑time model developer
Job Responsibility
Job Responsibility
  • Design, operate, and scale GPU and CPU clusters for ML training and inference (Slurm, Kubernetes, autoscaling, queueing, quota management)
  • Automate infrastructure provisioning and configuration using infrastructure‑as‑code (Terraform, CloudFormation, cluster‑tooling) and configuration management
  • Build and maintain robust ML pipelines (data ingestion, training, evaluation, deployment) with strong guarantees around reproducibility, traceability, and rollback
  • Implement and evolve ML‑centric CI/CD: testing, packaging, deployment of models and services
  • Own monitoring, logging, and alerting across training and serving: GPU/CPU utilization, latency, throughput, failures, and data/model drift (Grafana, Prometheus, Loki, CloudWatch)
  • Work with terabyte‑scale datasets and the associated storage, networking, and performance challenges
  • Partner closely with ML engineers and researchers to productionize their work, translating experimental setups into robust, scalable systems
  • Participate in on‑call rotation for critical ML infrastructure and lead incident response and post‑mortems when things break
What we offer
What we offer
  • Intellectually stimulating work environment
  • Be a pioneer: you get to work with realtime data processing & AI
  • Work in one of the hottest AI startups, with exciting career prospects
  • Team members are distributed across the world
  • Responsibilities and ability to make significant contribution to the company’s success
  • Inclusive workplace culture
  • Fulltime
Read More
Arrow Right

Senior ML Engineer- Distillation

A seasoned Senior ML Engineer who drives distillation of ML Models for high-perf...
Location
Location
Poland , Warsaw
Salary
Salary:
Not provided
amd.com Logo
AMD
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 6–10+ years in ML engineering or applied research
  • 3+ years focused on model distillation/compression at production scale
  • Strong proficiency in PyTorch (preferred) or JAX/TF
  • Ability to implement custom training loops, distributed training, and mixed precision
  • Demonstrated experience shipping distilled or compressed models to production with measurable gains in latency/memory and maintained quality
  • Deep understanding of knowledge distillation techniques: teacher–student frameworks, soft-labels, intermediate feature matching, contrastive distillation, task-specific loss shaping
  • Hands-on experience with quantization (static/dynamic, PTQ/QAT), pruning, and graph-level optimizations (operator fusion)
  • GPU performance engineering: CUDA fundamentals, TensorRT/ONNX Runtime, kernel profiling (Nsight), memory/layout optimization
  • Solid grasp of computer graphics fundamentals: rendering pipeline, shaders, sampling, anti-aliasing, tone mapping, and perceptual metrics
  • Strong software engineering: Python/C++ proficiency, testing, code quality, version control, reproducible pipelines, containerization
Job Responsibility
Job Responsibility
  • Distillation and compression: KD variants, hint/fitnets, attention transfer, feature mimicking, low-rank/SVD, sparsity
  • Efficient architectures: MobileNet/EfficientNet, vision transformers optimization, lightweight diffusion/UNet variants, NeRF/instant-NGP distillation
  • Inference optimization: TensorRT, CUDA, cuDNN, ONNX, quantization-aware training, weight clustering, operator fusion
  • Metrics: SSIM, LPIPS, PSNR, FID/KID, latency/throughput profiling, memory/activation footprint analysis
  • Data and training: large-scale dataset curation, synthetic data generation, curriculum learning, augmentation strategies
  • MLOps: experiment tracking, CI/CD for models, model registries, reproducibility, telemetry
  • Integrate ML inference into production rendering pipelines: define model I/O, preprocessing/postprocessing, and make trade-offs for latency, throughput, and quality
  • Collaborate across teams (ML researchers, engine/platform, tooling, QA) to translate ML and product requirements into graphics-friendly implementations and integration plans
  • Mentor other engineers, conduct code reviews, and help define best practices for rendering, performance, and SDK delivery
Read More
Arrow Right

Senior Research Engineer

As a Senior Research Engineer at Microsoft, you will help advance Microsoft’s mi...
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
  • Proficiency in Python and at least one deep learning framework such as PyTorch, JAX, or TensorFlow
  • Experience deploying Fine Tuned LLMs or multimodal models in live production environments
  • Experience shipping and maintaining production AI systems
  • 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
Job Responsibility
  • Build AI-First Contact Center Experiences
  • Bringing State-of-the-Art Research to Products
  • Design and implement AI systems using foundation models, prompt engineering, retrieval-augmented generation, multi-agent architectures, and classic ML
  • Fine-tune large language models on domain-specific data and evaluate via offline and online methods such as A/B testing, telemetry, and shadow deployments
  • Build and harden prototypes into production-ready services using robust software engineering and MLOps practices
  • Drive original research and thought leadership (whitepapers, internal notes, patents)
  • convert insights into shipped capabilities
  • Research Translation: Continuously review emerging work
  • identify high-potential methods and adapt them to Microsoft problem spaces
  • Partner with product teams to improve customer and agent outcomes
  • Fulltime
Read More
Arrow Right

Senior Research Engineer

As a Senior Research Engineer at Microsoft, you will advance Microsoft’s mission...
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, Engineering, Mathematics, Statistics, Physics, or a related field and 4 or more years in applied ML or AI research and product engineering
  • OR Master’s degree and 3 or more years in applied ML or AI research and product engineering
  • OR PhD in a relevant field and 2 or more years with generative AI, LLMs, or related ML algorithms
  • Ability to meet Microsoft, customer and/or government security screening requirements
  • Microsoft Cloud Background Check upon hire/transfer and every two years thereafter
Job Responsibility
Job Responsibility
  • Bringing State-of-the-Art Research to Products
  • Design and implement AI systems using foundation models, prompt engineering, retrieval-augmented generation, multi-agent architectures, and classic ML
  • Fine-tune large language models on domain-specific data and evaluate via offline and online methods such as A/B testing, telemetry, and shadow deployments
  • Build and harden prototypes into production-ready services using robust software engineering and MLOps practices
  • Drive original research and thought leadership (whitepapers, internal notes, patents)
  • convert insights into shipped capabilities
  • Research Translation: Continuously review emerging work
  • identify high-potential methods and adapt them to Microsoft problem spaces
  • End-to-End System Development
  • ML Design & Architecture: Own end-to-end pipeline from data prep, training, evaluation, deployment, and feedback loops
  • Fulltime
Read More
Arrow Right
New

Senior AI Research Engineer

Adyen is building a world-class AI team to redefine what intelligent systems can...
Location
Location
Spain , Madrid
Salary
Salary:
Not provided
adyen.com Logo
Adyen
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 6+ years of hands-on experience in applied AI/ML research or engineering, with a clear track record of shipping AI systems, including agentic or LLM-powered systems, in production environments
  • Deep expertise in language models and Generative AI, with hands-on depth across several of: architecture, post-training (fine-tuning, RLHF), inference optimization, context engineering, and failure modes at scale
  • Proven experience designing and operating agentic systems at scale, multi-agent orchestration, tool use, memory and context management, state handling for long-running workflows, and human-in-the-loop design
  • Rigorous and systematic about evaluation
  • Strong foundation in classical machine learning: supervised learning, ensemble methods, optimization, probabilistic modeling, and statistics
  • Write clean, well-structured, production-ready code, primarily Python
  • Hands-on experience with at least one production-grade agentic framework
Job Responsibility
Job Responsibility
  • Design and Deploy AI Agents for Complex Tasks
  • Own Evaluation and Benchmarking
  • Provide AI Expertise Across the Organization
  • Raise the Bar
Read More
Arrow Right

Senior Backend Engineer, Inference Platform

Together AI is building the Inference Platform that brings the most advanced gen...
Location
Location
United States , San Francisco
Salary
Salary:
160000.00 - 250000.00 USD / Year
together.ai Logo
Together AI
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • 5+ years of demonstrated experience building large-scale, fault-tolerant, distributed systems and API microservices
  • Strong background in designing, analyzing, and improving efficiency, scalability, and stability of complex systems
  • Excellent understanding of low-level OS concepts: multi-threading, memory management, networking, and storage performance
  • Expert-level programming in one or more of: Rust, Go, Python, or TypeScript
  • Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience
Job Responsibility
Job Responsibility
  • Build and optimize global and local request routing, ensuring low-latency load balancing across data centers and model engine pods
  • Develop auto-scaling systems to dynamically allocate resources and meet strict SLOs across dozens of data centers
  • Design systems for multi-tenant traffic shaping, tuning both resource allocation and request handling — including smart rate limiting and regulation — to ensure fairness and consistent experience across all users
  • Engineer trade-offs between latency and throughput to serve diverse workloads efficiently
  • Optimize prefix caching to reduce model compute and speed up responses
  • Collaborate with ML researchers to bring new model architectures into production at scale
  • Continuously profile and analyze system-level performance to identify bottlenecks and implement optimizations
What we offer
What we offer
  • Competitive compensation
  • equity
  • health insurance
  • other competitive benefits
  • Fulltime
Read More
Arrow Right

Senior AI Engineer

As a Senior AI Engineer focused on agentic framework, you will focus on building...
Location
Location
Denmark , København
Salary
Salary:
Not provided
life-science-talent-solutions.dk Logo
Life Science Talent
Expiration Date
Until further notice
Flip Icon
Requirements
Requirements
  • Strong programming skills in Python and the ability to contribute to production-grade codebases
  • Hands-on experience in LLMs, including at least some of the following: Training, finetuning, or post-training transformer-based models
  • Building or operating LLM inference services in production, including performance work
  • Experience with embeddings, vector databases, and semantic search
  • Practical experience implementing RAG architectures
  • Designing robust evaluations for agent workflows and generative systems, including metrics, error analysis, and human evaluation methods
  • Experience building production-grade ML systems that can be deployed and operated, including pipelines, CI and CD practices, and monitoring
  • Strong product mindset with the ability to translate ideas into working systems
  • Clear communication and collaboration skills across research, engineering, and product
  • A Master’s degree in computer science, engineering, mathematics, statistics, physics, or a related field, or equivalent professional experience
Job Responsibility
Job Responsibility
  • Design and build LLM-powered product features used in production
  • Develop agentic workflows and frameworks that coordinate multiple AI components
  • Implement RAG architectures using embeddings and vector search
  • Build systems for prompting, context engineering, and tool usage
  • Develop evaluation frameworks to measure LLM and agent performance
  • Work closely with product and platform teams to turn AI capabilities into reliable, scalable product features
  • Continuously improve system reliability, latency, and cost efficiency of AI pipelines
What we offer
What we offer
  • Equipment provided by Corti
  • Fulltime
Read More
Arrow Right