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Machine Learning Engineer at Cosine.sh

Posted 2 hours 35 minutes ago by Jack & Jill/External ATS

£70,000 - £90,000 Annual
Permanent
Full Time
Other
London, United Kingdom
Job Description

This is a job that we are recruiting for on behalf of one of our customers.

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Machine Learning Engineer Company Description: Cosine.sh is pioneering AI for software engineering, building advanced open-source-based models that empower developers with intelligent, real-world coding capabilities.

Job Description: Join Cosine.sh as a Machine Learning Engineer and lead large-scale training of Lumen Enterprise models-our flagship software engineering LLMs. You will drive state-of-the-art performance through supervised fine-tuning and reinforcement learning, operating close to the metal with PyTorch, distributed systems, and long-context architectures. This is a high-impact role where your work will directly shape the capabilities engineers depend on daily.

Location: London, UK

Why this role is remarkable:
  • Directly influence the next generation of Lumen Enterprise SWE models used by engineers every day.
  • Operate at real scale: modern open-source models, long-context training, MoE architectures, and multi-node GPU clusters.
  • A true full-stack ML engineering role combining PyTorch, distributed systems, data pipelines, RL design, and MLOps.
What you will do:
  • Transform open-source base models into high-performance Lumen Enterprise SWE agents using supervised fine-tuning and RL.
  • Design and execute large-scale training experiments on multi-node GPU clusters, including long-context and MoE training.
  • Build and refine large-scale RL loops where models write code, run tools/tests, and receive reward signals to improve performance.
The ideal candidate:
  • 3-5+ years of experience training deep learning models in production environments.
  • Deep proficiency with PyTorch and strong hands-on experience with torch.distributed for multi-GPU and multi-node training.
  • Experience training large sequence models or LLMs (70B+ parameters), with practical understanding of scaling challenges.
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