Cohere: Member of Technical Staff, Post-Training in New York

Neolife Updates; New York (NY); Remote; USD 150,000 - 240,000; Full time

Benefits offered by this job

Health and dental coverage

401(k) matching

Parental leave top-up

Meal stipend

Remote-friendly with global offices

Job summary

Cohere is seeking a Member of Technical Staff, Post-Training to join its team in New York, NY on a full-time basis. In this role you will advance the state of the art for foundation model post-training and ship frontier models directly to production, bridging deep learning research with scalable enterprise software.

Cohere operates with one of the highest compute-to-engineer ratios in the AI industry, maintaining an environment without rigid boundaries between research and production

Qualifications

• Strong software engineering with production-quality focus.
• Experience with large-scale distributed training and ML frameworks.
• Post-training management of large models and optimization.
• Publications in top ML venues are a bonus.

Responsibilities

• Design scalable software for training large-scale models.
• Post-train models to achieve SOTA benchmarks.
• Develop algorithms to improve training cycles in SFT and RL.
• Collaborate with Code and Agentic teams to build robust models.
• Experiment with supercomputing infrastructure and accelerators.

Skills

Production code

Python

PyTorch

JAX

XLA/MLIR

Kubernetes

Ray

Slurm

Distributed training

Post-training focus

Job description

Cohere is seeking a Member of Technical Staff, Post-Training to join its team in New York, NY on a full-time basis. In this role, you will advance the state of the art for foundation model post-training and ship frontier models directly to production, bridging deep learning research with scalable enterprise software.

Role Overview and Engineering Culture at Cohere

Cohere operates with one of the highest compute-to-engineer ratios in the AI industry, maintaining an environment without rigid boundaries between research and production engineering. Rather than isolating experimental ideas, every technical team member writes production-grade code, tests concepts across supercomputing clusters, and deploys high-impact models for enterprise adoption. While aligned with the New York City office, the position supports remote-friendly flexibility with no mandatory in-office attendance requirement.

Key Responsibilities

• Scalable Software Architecture: Design and implement high-performance, fault-tolerant software for training large-scale models.
• Post-Training Optimization: Consistently post-train models to achieve state-of-the-art (SOTA) benchmarks on real-world business tasks.
• Algorithmic Development: Innovate and implement techniques to enhance training cycle results across Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) regimes.
• Cross-Functional Collaboration: Partner closely with dedicated specialist teams, including Code and Agentic systems, to produce well-rounded, capable models.
• Supercompute Experimentation: Research, implement, and validate novel ideas across dedicated data infrastructure and distributed accelerators.

Candidate Qualifications and Technical Background

• Core Engineering: Exceptionally strong software engineering skills with an emphasis on production quality.
• ML Frameworks: High proficiency in Python and modern frameworks, including PyTorch, JAX, and XLA/MLIR.
• Distributed Infrastructure: Hands-on experience with orchestration and distributed training frameworks, such as Kubernetes, Slurm, and Ray.
• At-Scale Training: Demonstrated track record executing large-scale distributed training strategies.
• Post-Training Specialization: Direct experience managing the post-training phase of large models, with a dedicated focus on performance optimization.
• Research Record (Bonus): Publications at premier machine learning venues such as NeurIPS, ICML, ICLR, MLSys, ACL, or EMNLP.

Full-Time Benefits and Perks

• Health and Wellness: Comprehensive health and dental coverage, including a dedicated mental health budget.
• Retirement Programs: 401(k), RRSP matching, or pension scheme participation based on location.
• Parental Support: 100% parental leave top-up for up to six months for either parent.
• Meal Allowance: A weekly lunch stipend of $75/£75 (or local currency equivalent).
• Workplace Flexibility: Remote-friendly work options alongside access to key global offices, with zero minimum in-office requirements.

Application Tips for Post-Training Candidates

• Highlight Distributed Scaling: Explicitly detail cluster configurations you have managed, including accelerator types, interconnect considerations, and frameworks like Ray or Slurm.
• Showcase Alignment and Optimization: Detail specific SFT or RL implementations where your architectural adjustments improved training efficiency or downstream model metrics.
• Reference Public Technical Artifacts: Highlight any open-source model repositories, systems code, or peer-reviewed publications relevant to large-scale deep learning.

This post-training position offers a high-impact opportunity to push the limits of generative AI models backed by extensive supercomputing infrastructure and a collaborative research environment.