ML Systems Engineer - Model Training and Infrastructure (SWE-focused LLMs)
Salary not listed • London Office • Full-time
Posted 22d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
We are seeking an ML Systems Engineer to contribute to the training of our Lumen LLMs, which are based on open-source software engineering models. This role is interdisciplinary, involving the development and deployment of reinforcement learning training environments, working with large-scale synthetic data pipelines, and building an ML platform for training large models in distributed settings. The goal is to train models that not only provide correct answers but also write readable, maintainable code aligned with architectural patterns. This position is ideal for someone passionate about the evolving landscape of coding agents, where data, RL environments, and opinionated reward functions are shaping future SWE model standards.
Responsibilities
- Participate in end-to-end model training, including supervised fine-tuning and RL alignment for software engineering objectives.
- Develop and manage synthetic data generation pipelines for training datasets.
- Design, build, and deploy containerized services using Docker and Kubernetes for RL infrastructure.
- Build and maintain a training platform for large-scale model architecture experiments, training, and evaluations.
- Ideate on novel reward functions for training SWE agents.
- Collaborate with infra, product, and research teams to define training priorities, methods, and evaluation strategies.
Requirements
- Strong software engineering or computer science background with typically 3-5 years of experience.
- Proficiency in reading, debugging, and writing production code, primarily in Python and Go.
- Experience with containerization tools like Docker and orchestration platforms like Kubernetes.
- Experience with at least one major cloud-computing platform (GCP, AWS, or Azure).
- Commitment to code quality, correctness, and maintainability alongside model metrics.
- Data engineering skills, including experience with large-scale datasets, object storage, dataset sharding, and filtering.
- Understanding of the importance of data quality and sampling strategies.
- Ability to translate vague modeling goals into concrete experiment plans.
- Strong communication skills for documenting decisions and explaining tradeoffs.
Benefits
- Competitive salary benchmarked to the market
- Equity / share options
- 30 days' holiday + bank holidays
- High-quality equipment
- Dog-friendly office
- Weekly team breakfast & lunch
- Monthly socials
- Pension