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Cohere Logo Cohere · IT - Software Development

Senior ML Systems Engineer, Frameworks & Tooling

· Job Posted February 20, 2026
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Job Description

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. If you enjoy working across the full stack of ML systems, this role gives you the opportunity and autonomy to have massive impact.

Job Responsibility

  • Build and own the training framework responsible for large-scale LLM training
  • Design distributed training abstractions (data/tensor/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 our cluster, 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

Requirements

  • 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
  • Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP)

What we offer

  • 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!)
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