ML Research Engineer, Foundation Models (Senior / Staff / Principal)

About the role

ML Research Engineer, Foundation Models

About the Role

This role is for a highly skilled ML Research Engineer who thrives at the intersection of fundamental research and production-grade engineering.

As a core member of the Genesis AI team, you will serve as the engineering pillar for inventing, scaling, and shipping our next generation of foundation models for molecular science.

You will partner closely with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge model ideas into systems that power real drug discovery programs.

Your work may involve

• Scaling model pretraining pipelines
• Advancing reinforcement learning or post-training systems
• Optimizing performance of large molecular models
• Bringing structure prediction models like Pearl into production environments used by chemists and drug programs

This role requires someone who can bridge ML and computational chemistry, translating between disciplines and helping teams move quickly from research insight to deployed capability.

We are looking for someone who can own problems end-to-end, in a fast-moving research environment, translating novel ML ideas into systems that scientists can use in active discovery programs.

Positions are available at various levels of seniority: Senior, Staff, and Principal.

You Will

• 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. You'll design experimental plans, own their execution on our large-scale compute infrastructure, and drive the deep analysis of results to inform the next research cycle and to validate most promising approaches.
• Ship state-of-the-art models to production,
• Collaborate intensely. Work closely with the broader team to integrate your models into our drug discovery platform.
• Mentor and guide other researchers and engineers, fostering a culture of high-quality code, rigorous experimentation, and continuous innovation.
• Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.

Who You Are

• 2+ years industry experience of building complex ML systems.
• A research engineer with deep ML rigor.

Additional information

You have deep expertise in building scalable, high-performance foundation models, pretraining, and posttraining methods, and systems around them.

• A builder who ships.

You write clean, high-performance code and are comfortable working across the ML stack (Python, PyTorch, distributed training systems). You have demonstrated experience translating research into working systems quickly.

• An expert in modern ML engineering.

You understand the mathematics and systems behind modern ML methods. You can design, optimize, and implement novel modeling approaches.

• Experienced in training models at scale.

You understand distributed training, large-scale datasets, and performance optimization across GPU clusters. You thrive in environments where models move rapidly from prototype to production.

• Experience with GPU systems programming

Hands-on experience writing CUDA kernels or optimizing GPU workloads beyond standard frameworks.

• Hands-on experience with our core libraries: PyTorch, PyTorch Lightning, and Ray Distributed Training, PyTorch Geometric, etc.
• Comfortable in research ambiguity.

You can iterate on novel architectures, training pipelines, and experimental ideas while maintaining rigorous engineering discipline.

• A first-principles thinker.

You approach problems from fundamentals and take pride in building robust systems from conceptual design to state-of-the-art implementation.

• A curious mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
• A strong cross-functional collaborator.

You communicate effectively with scientists across disciplines including computational chemistry, structural biology, and medicinal chemistry.

• No prior biology or chemistry experience is required, though curiosity and willingness to learn are essential.

Nice to haves

• Experience with novel research in one or more of the following domains: LLMs, diffusion, reinforcement learning or other cutting edge generative or predictive machine learning models.
• Computational chemistry or drug discovery systems

Additional information

Especially experience related to protein-ligand structure prediction, small-molecule modeling, or computational drug discovery workflows.

• Generative modeling methods

Diffusion models or other generative architectures applied to scientific or molecular problems.

• LLM post-training techniques

Experience with SFT, RLHF, synthetic data pipelines, or other post-training systems.

• Performance engineering

Experience with Triton kernels, TensorRT, quantization, or large-scale model serving.

• Publications in top-tier ML venues

NeurIPS, ICML, ICLR, or similar.

• Experience with ML frameworks used at Genesis

PyTorch, PyTorch Lightning, Ray Distributed Training, PyTorch Geometric, or related systems.

• Advanced degree

MS or PhD in machine learning, computer science, computational science, or equivalent research/engineering experience.

What we offer

• Competitive compensation package that includes salary and equity.
• Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
• 401(k) plan.
• Open (unlimited) PTO policy.
• Free lunches and dinners at our offices.
• Paid family leave (maternity and paternity).
• Life and long- and short-term disability insurance.