Member of Technical Staff, Mle [singapore]

Cohere Cohere · AI Frontier · Singapore · Modeling

Member of Technical Staff, Applied ML at Cohere, focusing on building and customizing LLM solutions for enterprise customers. This role involves training and customizing frontier models, integrating retrieval and agent capabilities, and contributing to the development of Cohere's foundation models based on customer insights. The position requires a blend of applied ML, customer-facing engineering, and core model influence.

What you'd actually do

  1. Work directly with enterprise customers on problems that push LLMs to their limits.
  2. Train and customize frontier models — not just use APIs.
  3. Influence the capabilities of Cohere’s foundation models.
  4. Operate with an early-startup level of ownership inside a frontier-model company.
  5. Wear multiple hats, set a high technical bar, and define what Applied ML at Cohere becomes.

Skills

Required

  • Python
  • ML/LLM frameworks
  • large-scale datasets
  • distributed training or inference pipelines
  • LLM architectures
  • tuning techniques (CPT, post-training)
  • evaluation methodologies
  • ML fundamentals
  • Python
  • ML/LLM frameworks
  • large-scale datasets
  • distributed training or inference pipelines
  • LLM architectures
  • tuning techniques (CPT, post-training)
  • evaluation methodologies

Nice to have

  • RLVR
  • retrieval + agent integrations

What the JD emphasized

  • production-ready models
  • customer domains
  • custom LLM solutions
  • frontier models
  • CPT
  • post-training
  • retrieval + agent integrations
  • model evaluations
  • SOTA modeling techniques
  • foundation models
  • custom models
  • CPT recipes
  • post-training pipelines
  • RLVR
  • data assets
  • SOTA modeling techniques
  • foundation-model stack
  • tuning strategies
  • evaluation frameworks
  • customer facing MLE team
  • enterprise customers
  • ML fundamentals
  • LLM solutions
  • Python
  • ML/LLM frameworks
  • large-scale datasets
  • distributed training
  • inference pipelines
  • LLM architectures
  • tuning techniques
  • CPT
  • post-training
  • evaluation methodologies
  • LLM performance
  • ML research landscape
  • state of the art

Other signals

  • customer-facing
  • custom models
  • foundation models
  • LLM solutions