relationrx - Senior Machine Learning Research Engineer
Requirements
• 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 • Personally, you • Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams. • balancing multiple stakeholders • Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work. • ownership of your work • Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect. • Communicate openly and directly • Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams. • Actively seek out diverse perspectives • Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes. • Embrace challenges with openness and resilience • Working Style & Culture at Relation • At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together! The patient is waiting! • matrixed, interdisciplinary environment • The patient is waiting! • Recruitment Agencies • Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.
Responsibilities
• 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. • 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.
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