ML Research Engineer, Foundation Models (Senior / Staff / Principal)
Hybrid (San Mateo, CA), Hybrid (New York, NY)Full-timePosted 3mo ago
About the role
Senior-level ML Research Engineer role focused on building, scaling, and deploying foundation models for molecular science in a hybrid, cross-disciplinary environment.
Responsibilities
- Drive the R&D and scaling of our foundation models, taking ownership of the engineering and experimentation for key research initiatives.
- Make cutting-edge foundation model research a reality at scale. Implement, optimize, and build novel foundation models from the initial research prototypes to high-performance production models.
- Optimize performance of large-scale ML systems, including distributed training, inference efficiency, and GPU-level optimizations where necessary.
- Constantly engage with deep learning literature, building upon novel architectures and training methods to create new capabilities.
- Bridge machine learning research and computational chemistry workflows, working closely with computational chemists, structural biologists, and medicinal chemists to ensure models translate effectively into real drug discovery programs.
- Help productionize Pearl and related structure prediction models, enabling reliable deployment and integration into Genesis’ internal and partner drug discovery pipelines.
- Own the experimental lifecycle with scientific rigor. Design experimental plans, own their execution on large-scale compute infrastructure, and drive deep analysis of results to inform the next research cycle.
- Ship state-of-the-art models to production
- Collaborate intensely and integrate models into the drug discovery platform
- Mentor and guide other researchers and engineers
- Contribute to the global research community by publishing work and representing Genesis at AI/ML conferences
Qualifications
- 2+ years industry experience of building complex ML systems.
- A research engineer with deep ML rigor and expertise in scalable, high-performance foundation models, pretraining, and posttraining methods, and related systems.
- Proficient across ML stack (Python, PyTorch, distributed training systems).
- Experience training models at scale, distributed training, large-scale datasets, and GPU optimization.
- Experience with GPU systems programming, including CUDA kernels or optimizing GPU workloads beyond standard frameworks.
- Hands-on experience with core libraries: PyTorch, PyTorch Lightning, Ray Distributed Training, PyTorch Geometric, etc.
- Comfortable with research ambiguity and iterative development of novel architectures and pipelines.
- First-principles thinker with curiosity about AI, physics, chemistry, and biology; strong cross-functional collaboration.
- No required biology/chemistry background, but willingness to learn is essential.
- Strong ability to communicate with scientists across disciplines.
Skills
Required:Foundation model developmentDistributed trainingGPU optimizationPyTorchCross-functional collaboration
Nice to have:Ray Distributed Training
Benefits
- Competitive compensation package including salary and equity.
- Comprehensive health benefits: Medical, Dental, and Vision (100% coverage for employees).
- 401(k) plan.
- Open (unlimited) PTO policy.
- Free lunches and dinners at offices.
- Paid family leave (maternity and paternity).
- Life and long- and short-term disability insurance.
Last updated Jul 20, 2026. Interested? Apply on the company site.
About Genesis Molecular AI
The frontier AI for drug discovery
Genesis pairs frontier AI with world-class drug hunters to discover small molecule medicines. Built on Pearl and GEMS. Partnered with Gilead and Incyte.
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