ML Infrastructure Architect for RL & Post-Training

Stealth - AI x Bio; Greater London; In loco; GBP 89.000 - 134.000; Tempo pieno

Vantaggi offerti da questo lavoro

30 days annual leave + public holidays

UK pension 10% employer contribution +

Descrizione del lavoro

Stealth - AI x Bio is building foundational infrastructure for AI and biology data generation. The role focuses on end-to-end development of the post-training and reinforcement learning stack, with emphasis on training, inference serving, and scalable pipelines used by researchers and agents.

You will own the stack from design to deployment, establish engineering standards, and drive reliability as workloads scale.

Competenze

• Experience building infrastructure for LLM inference or RL training.
• Systems fundamentals; ability to profile slow or failing workloads to root causes.
• Hands-on work with GPU clusters, distributed training, model serving or high-throughput inference (scheduling, utilisation, cost).
• Good judgement on technical trade-offs (prototype vs hardened system).
• Clear writing and communication about system design and engineering trade-offs.
• Fluent Python and daily use of coding agents.

Mansioni

• Own end-to-end stack design, implementation, deployment and maintenance.
• Set engineering standards including testing, observability and versioning.
• Improve performance, scalability and reliability as workloads grow.
• Collaborate with RL, evaluation and Bio ML researchers and the data-capture software team.
• Ensure reproducibility across data versions and training/evaluation runs.

Conoscenze

LLM inference infra

Systems profiling

GPU clusters

Technical trade-offs

Clear communication

Python

Strumenti

Docker

Kubernetes

Ray

Terraform

Descrizione del lavoro

Stealth - AI x Bio is building foundational infrastructure for AI and biology data generation. The role focuses on end-to-end development of the post-training and reinforcement learning stack, with emphasis on training, inference serving, and scalable pipelines used by researchers and agents.

You will own the stack from design to deployment, establish engineering standards, and drive reliability as workloads scale.