ML Research Engineer
SandboxAQAI & company
United KingdomSenior
T. Rowe Price
Breyer Capital
NVIDIA
Eric Schmidt
Google (Alphabet)
TIME Ventures
Data & AI
About the role
TL;DR
Develop and optimize ML models for drug discovery.
- •As a ML Research Engineer, you will bring cutting-edge research into production-grade reality, tasked with making in silico design the dominant paradigm in drug discovery.
- •Your central purpose is to architect, scale, and optimize the scientific codebases that power our LQMs.
- •Key Responsibilities Research, Architect, and Scale: Bring novel ideas and the content of scientific papers into high-performing and robust scientific code.
- •ML Engineering: Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into our large-scale simulation frameworks.
- •Ownership of the Lifecycle: Drive software through the entire product lifecycle—from foundational research and implementation to launch and long-term support—ensuring technical excellence at every stage.
- •Requirements Academic Foundation: PhD, or research-focused MSc, in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods.
- •Software Excellence: Staff (5+ years) industry experience developing productionized software in professional teams.
- •Structural Biology Exposure: Experience or training in data-science related tasks related to structural biology.
- •Research Translation: Expertise translating research papers into concrete ML software artifacts.
- •Product Lifecycle Mastery: Experience supporting models in external-facing products, demonstrating the ability to bridge the gap between "research code" and "product code".
Required skills
PythonTensorFlowscikit-learnPandasNumPy
Domain expertise
biotech
Benefits & perks
Compensation, Benefits, Work-Life Balance, Career Development
Tech stack
PythonTensorFlowscikit-learnPandasNumPyAirflowKubernetesCI/CDAWSGoogle Cloud