> Markdown version of [/jobs/ext/3809771-machine-learning-research-engineer](https://www.wearedevelopers.com/jobs/ext/3809771-machine-learning-research-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Research Engineer

- **Company:** Postaladdress
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Cloud Computing, Profiling, Nvidia CUDA, Distributed Computing Environment, Python (Programming Language), Linux Kernel, Open Source Technology, Tensorflow, Pytorch, Large Language Models, Information Technology, Machine Learning Operations
- **Published:** October 18, 2026
- **Apply:** https://startup.jobs/machine-learning-research-engineer-relationrx-8978322

## About the Role

* A degree in Computer Science, Engineering, Physics, or a related quantitative discipline; industry experience as an ML / research engineer working on large neural network training.
 * Strong software engineering fundamentals in Python and deep expertise in PyTorch (or equivalent modern ML frameworks).
 * Hands-on experience training large neural networks at scale, including distributed training frameworks.
 * Demonstrable experience profiling and optimising GPU workloads.
 * Working knowledge of cloud-based ML infrastructure and containerised environments.
 * A track record of taking research code from prototype to robust, reusable infrastructure that other people actually use.
Bonus experience:

 * CUDA / Triton kernel development; FlashAttention-style attention implementations; experience with foundation models for biology, vision, or language; contributions to open-source ML frameworks.

Personally, you

 * Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams.
 * Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work.
 * Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect.
 * Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams.
 * Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes.

## Description

We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact., * Implement and optimise large models, partnering with ML Scientists to translate research ideas into reproducible, scalable training pipelines.

 * Profile and optimise training across compute, memory, and I/O, pursuing measurable gains in throughput, convergence, and stability.
 * Design and implement distributed training strategies across multi-GPU and multi-node configurations.
 * Build and maintain core ML infrastructure.
 * Contribute to architectural and algorithmic decisions, bringing engineering judgment into research discussions.
 * Optimise inference and downstream deployment so models can be used by data scientists and biologists in our discovery workflows.
 * Address numerical, performance, and reliability issues across the stack.
 * Establish and maintain engineering practices in research code.
 * Track developments in ML systems and bring relevant advances into our stack.