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.
  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
  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.

About Genesis Molecular AI

Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes and GEN) with a total potential deal value of several billion dollars.

Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.