Member of Technical Staff, Mle (north)

Cohere Cohere · AI Frontier · London, United Kingdom · Modeling

Cohere is seeking a Member of Technical Staff to join their North team, focusing on building and scaling AI systems for enterprise products. The role involves driving agent development in RAG, tool use, and language agents, translating research into products, and collaborating on data for post-training LLMs.

What you'd actually do

  1. Join a small, diverse team of engineers in designing, building, and scaling AI systems that underpin our suite of dev-centric enterprise products.
  2. Work directly on North, Cohere’s all-in-one secure AI workspace platform. Here you will drive agent development in RAG, tool use, and language agents embedded in North.
  3. Quickly research and experiment with novel ideas on our supercomputer and data infrastructure, ensuring our products remain at the forefront of the industry.
  4. Collaborate with top researchers, engineers, and annotators to create and evaluate data for post-training LLMs, ensuring our products are of the highest quality and performance.
  5. Engage with the latest AI and deep learning research, staying up to date with leading conferences such as NeurIPS, ICLR, and AAAI.

Skills

Required

  • Extremely strong software engineering skills.
  • Proficiency in Python and related ML frameworks such as Tensorflow, TF-Serving, JAX, and XLA/MLIR.
  • Direct experience working as part of a team building Large Language Models.
  • Released multiple features with several iterations.
  • Strong track record of creating and curating large-scale datasets.
  • Experience using large-scale distributed training strategies.
  • Familiarity with autoregressive sequence models, such as Transformers.
  • Ability to collaborate effectively with human annotators and cross-functional teams.

Nice to have

  • Deep experience in building and leading a product-centric organisation.
  • Paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).

What the JD emphasized

  • direct experience working as part of a team building Large Language Models
  • Released multiple features with several iterations
  • Strong track record of creating and curating large-scale datasets
  • Paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP)

Other signals

  • building and scaling AI systems
  • drive agent development
  • translate cutting edge research into tangible, high-performance products