ML Engineer (£80k-£110k + Equity) at Cosine.sh at Jack & Jill/External ATS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

12 Jun, 26

Salary

110000.0

Posted On

14 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SFT, RL, Continued Pretraining, PyTorch, FSDP, DDP, Large-scale Model Training, RLVR, LLM Alignment, Python, Data Quality, Sampling Strategies, MoE Architectures, Multi-node GPU Clusters, PPO, DPO

Industry

Staffing and Recruiting

Description

This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers. She will pick the best candidates from Jack's network. The next step is to speak to Jack. ML Engineer (£80k-£110k + Equity) at Cosine.sh Company Description: Cosine.sh - YC-backed AI startup Job Description: Join a high-intensity ML team owning the post-training of Lumen Enterprise, the LLMs powering the world’s best autonomous coding agent. You will drive SFT, RL, and continued pretraining to push state-of-the-art performance on complex software engineering tasks. This role offers direct impact on a product used by global enterprises. Why this role is remarkable: Direct ownership of post-training for Genie, a SOTA coding agent that achieved a 72% score on OpenAI’s SWE-Lancer benchmark. Work at the technical frontier with multi-node GPU clusters, large-scale MoE architectures, and long-context training on proprietary software-engineering reasoning data. Join a small, elite 4-person ML team reporting directly to the CEO, where your training runs ship immediately to real-world enterprise users. What you will do: Transform open-source base models into high-performance SWE agents through supervised fine-tuning and advanced reinforcement learning (PPO, GRPO, or DPO). Design and execute large-scale training experiments on multi-node clusters, optimizing for long-context stability and tool-use reasoning. Build and iterate on automated RL loops where models are rewarded for successfully running tests, linters, and static analysis on real-world codebases. The ideal candidate: 3-5+ years of experience training deep learning models in production with deep proficiency in PyTorch distributed primitives like FSDP and DDP. Proven track record of training large-scale models (≥70B parameters) and implementing complex RLVR systems for LLM alignment. Strong software engineering background with the ability to write production-grade Python and a focus on data quality and sampling strategies. Who are Jack & Jill? Ok, I'll go first. I'm Jack, an AI Career Agent that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support. And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate. Next steps Step 1. Visit our website. Step 2. Click 'Talk to Jack'. Step 3. Talk to Jack so he can understand your experience and ambitions. Step 4. Jack will make sure Jill (the AI agent working for the company) considers you for this role. Step 5. If Jill thinks you're a great fit and her client wants to meet you, they will make the introduction. Step 6. If not, Jack will find you excellent alternatives. All for free. We never post fake jobs This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network. Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description. We appreciate this can make them look a bit suspect, but there isn't much we can do about it. Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.

Responsibilities

The role involves driving Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and continued pretraining to enhance the performance of Lumen Enterprise LLMs for complex software engineering tasks. Responsibilities include transforming open-source models into high-performance agents and building automated RL loops that reward successful code execution.