Research Engineer – Workflows​/Systems; CA, US - Hybrid

Are you passionate about designing scalable ML systems and enabling cutting-edge research at scale? This is an exciting chance to join a talented team shaping the future of AI-driven molecular modeling. We’re looking for an ML Research Engineer – Workflows/Systems, someone who thrives at the intersection of machine learning system design and distributed computing, and is eager to architect solutions that power next-generation foundation models in drug discovery.

The Employer

Join a forward-thinking, well-funded leader advancing AI x Chemistry innovation to make molecular biology something that can be learned, predicted, and designed. With a culture that champions speed, technical rigor, and collaborative problem‑solving, this organization provides an environment where ambitious engineers work alongside top scientists to make a real‑world impact. Operating on a frontier scale of data, compute, and innovation, you’ll have the opportunity to influence breakthrough technologies for healthcare’s future.

• 2+ years of relevant industry experience in ML systems engineering
• Strong proficiency in PyTorch, JAX, and modern ML infrastructure tools
• Experience implementing asynchronous workflows for ML pipelines
• Proven history of producing clean, maintainable, and well‑documented code (e.g., Git Hub repositories)
• Hands‑on experience building scalable and reliable ML systems

This role focuses on optimizing system architecture for large‑scale ML research and distributed compute.

• Build and manage robust asynchronous workflows for data generation, training, and evaluation
• Design and enhance overall ML systems architecture and optimize for scalability
• Troubleshoot and resolve performance bottlenecks as models increase in size and complexity
• Act as a bridge between research scientists and infrastructure teams to streamline work at scale

• Machine Learning Frameworks:
  PyTorch, JAX
• Workflow Design:
  Asynchronous programming, distributed systems
• Systems Architecture:
  Clean abstractions, consistent library design disciplines

• Experience with equivariant models, geometric deep learning, or GNNs
• Familiarity with workflow orchestration tools like Flyte or Dagster
• Comfort working with quantum chemical data pipelines or scientists in related domains

If you’re an ML systems expert looking to make a significant impact in a pioneering scientific organization, this is your opportunity. Apply today or call us on +44 1403 216216 to discuss this exciting role in more details.