Senior ML Systems Engineer, Frameworks & Tooling

Cohere; San Francisco (CA); Hybrid; USD 150,000 - 180,000; Full time

Benefits offered by this job

Inclusive culture

Weekly lunch stipend

Health and dental benefits

100% Parental Leave top-up

Personal enrichment benefits

Remote-flexible work

6 weeks of vacation

Job summary

A leading AI research firm located in San Francisco is seeking a Senior ML Systems Engineer to build and maintain the training framework for large-scale language models. The role involves designing distributed training solutions and improving training throughput across multi-node clusters. The ideal candidate will have strong engineering experience in distributed training, familiarity with JAX, and excellent collaboration skills. This position promises significant ownership over critical components and engagement with cutting-edge AI technologies while offering a flexible work environment.

Qualifications

• Strong engineering experience in large-scale distributed training or HPC systems.
• Experience with multi-node cluster orchestration.
• Comfort debugging performance issues across the ML systems stack.

Responsibilities

• Build and own the training framework for large-scale LLM training.
• Design distributed training abstractions.
• Improve training throughput on multi-node clusters.

Skills

Large-scale distributed training

HPC systems

Performance debugging

Containerized environments

Collaboration skills

Tools

JAX

CUDA/NCCL

Docker

Kubernetes

Job description

Senior ML Systems Engineer, Frameworks & Tooling at Cohere

Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.

Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products. We obsess over what we build and work hard and move fast to do what’s best for our customers. Join us on our mission and shape the future!

We’re looking for a senior engineer to help build, maintain and evolve the training framework that powers our frontier-scale language models. This role sits at the intersection of large‑scale training, distributed systems, and HPC infrastructure. You will design and maintain the core components that enable fast, reliable, and scalable model training and build the tooling that connects research ideas to thousands of GPUs.

Responsibilities

• Build and own the training framework responsible for large-scale LLM training.
• Design distributed training abstractions (data, tensor, and pipeline parallelism, FSDP/ZeRO strategies, memory management, checkpointing).
• Improve training throughput and stability on multi-node clusters (e.g., GB200/300, AMD, H200/100).
• Develop and maintain tooling for monitoring, logging, debugging, and developer ergonomics.
• Collaborate closely with infra teams to ensure Slurm setups, container environments, and hardware configurations support high-performance training.
• Investigate and resolve performance bottlenecks across the ML systems stack.
• Build robust systems that ensure reproducible, debuggable, large-scale runs.

You Might Be a Good Fit If You Have

• Strong engineering experience in large-scale distributed training or HPC systems. Deep familiarity with JAX internals, distributed training libraries, or custom kernels/fused ops.
• Experience with multi-node cluster orchestration (Slurm, Ray, Kubernetes, or similar).
• Comfort debugging performance issues across CUDA/NCCL, networking, IO, and data pipelines.
• Experience working with containerized environments (Docker, Singularity/Apptainer).
• A track record of building tools that increase developer velocity for ML teams.
• Excellent judgment around trade-offs: performance vs complexity, research velocity vs maintainability.
• Strong collaboration skills — you’ll work closely with infra, research, and deployment teams.

Nice to Have

• Experience with training LLMs or other large transformer architectures.
• Contributions to ML frameworks (PyTorch, JAX, DeepSpeed, Megatron, xFormers, etc.).
• Familiarity with evaluation and serving frameworks (vLLM, TensorRT-LLM, custom KV caches).
• Experience with data pipeline optimization, sharded datasets, or caching strategies.
• Background in performance engineering, profiling, or low-level systems.

Why Join Us

• You’ll work on some of the most challenging and consequential ML systems problems today.
• You’ll collaborate with a world‑class team working fast and at scale.
• You’ll have end-to-end ownership over critical components of the training stack.
• You’ll shape the next generation of infrastructure for frontier-scale models.
• You’ll build tools and systems that directly accelerate research and model quality.

Sample Projects

• Build a high-performance data loading and caching pipeline.
• Implement performance profiling across the ML systems stack.
• Develop internal metrics and monitoring for training runs.
• Build reproducibility and regression testing infrastructure.
• Develop a performant fault-tolerant distributed checkpointing system.

We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.

Full‑Time Employees At Cohere Enjoy These Perks

• 🤝 An open and inclusive culture and work environment
• 🧑💻 Work closely with a team on the cutting edge of AI research
• 🍽 Weekly lunch stipend, in‑office lunches & snacks
• 🦷 Full health and dental benefits, including a separate budget to take care of your mental health
• 🐣 100% Parental Leave top‑up for up to 6 months
• 🎨 Personal enrichment benefits towards arts and culture, fitness and well‑being, quality time, and workspace improvement
• 🏙 Remote‑flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co‑working stipend
• ✈️ 6 weeks of vacation (30 working days!)