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

Genesis Molecular AI; New York (NY); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Equity

Health benefits

401(k)

Unlimited PTO

Meals provided

Family leave

Disability insurance

Job summary

Genesis Molecular AI is seeking an ML Research Engineer to lead the engineering pillar for foundation models in molecular science. You will scale, ship, and productionize models that power drug discovery programs, partnering with researchers, chemists, and biologists.

You'll own the end-to-end lifecycle from pretraining to deployment, optimize distributed training and inference, and mentor other engineers while publishing work at top conferences.

Qualifications

• 2+ years industry experience building complex ML systems.
• Proven track record of shipping high-performance Python/PyTorch code and distributed training systems.
• Experience with training models at scale on large datasets and GPUs.

Responsibilities

• Drive the development and scaling of foundation models for molecular science.
• Implement, optimize, and productionize models from research prototypes.
• Optimize distributed training, inference, and GPU performance.
• Engage with literature and build new architectures for deployment.
• Bridge ML research with computational chemistry workflows to enable drug discovery.
• Help productionize Pearl and related structure prediction models for reliable deployment.
• Own the experimental lifecycle and drive data-informed iteration.
• Ship state-of-the-art models to production.
• Collaborate with scientists to integrate models into drug discovery platforms.
• Mentor researchers and engineers to uphold high-quality code and rigorous experimentation.
• Contribute to the research community via publications.

Skills

Python

PyTorch

PyTorch Lightning

Ray

CUDA

Distributed Training

Foundation Models

Education

MS or PhD in ML/CS

Tools

PyTorch Geometric

Triton

TensorRT

Job description

About the Team

Join a world‑class team at the forefront of AI and biochemistry at Genesis Molecular AI. Our tight‑knit group of deep learning researchers, software engineers, and drug discovery pioneers builds large‑scale generative models that harness machine learning, physics, and computational chemistry to unlock new therapies for patients with severe diseases.

About the Role

ML Research Engineer, Foundation Models
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

Responsibilities

• Drive the Rété développement and scaling of our foundation models, taking ownership of the engineering and experimentation for key research initiatives.
• Implement, optimize, and build novel foundation models from research prototypes to high‑performance production models.
• Optimize distributed training, inference efficiency, and GPU‑level performance of large‑scale ML systems.
• Engage with deep learning literature, build upon novel architectures, and create new capabilities.
• Bridge machine learning research and computational chemistry workflows, working closely with chemists, structural biologists, and medicinal chemists to ensure models translate effectively into real drug discovery programs.
• Help productionize Pearl and related structure prediction models for reliable deployment and integration into Genesis’ internal and partner pipelines.
• Own the experimental lifecycle with scientific rigor, designing experiments, executing them on large‑scale compute infrastructure, and driving deep analysis of results to inform the next research cycle and validate promising approaches.
• Ship state‑of‑the‑art models to production.
• Collaborate intensely with the broader team to integrate 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 work and representing Genesis at top‑tier AI/ML conferences and workshops.

Qualifications

• 2+ years industry experience building complex ML systems.
• Deep expertise in building scalable, high‑performance foundation models, pretraining, and posttraining methods, and systems around them.
• Proven track record of shipping code: clean, high‑performance Python/PyTorch, distributed training systems.
• Strong understanding of the mathematics and systems behind modern ML methods; able to design, optimize, and implement novel modeling approaches.
• Experience with training models at scale: distributed training, large‑scale datasets, and performance optimization across GPU clusters.
• Hands‑on experience with GPU systems programming (CUDA kernels or GPU workload optimization).
• Hands‑on experience with core libraries: PyTorch, PyTorch Lightning, Ray Distributed Training, PyTorch Geometric, etc.
• Comfortable in research ambiguity: iterate on architectures, pipelines, and experimental ideas while maintaining rigorous engineering discipline.
• First‑principles thinker: build robust systems from conceptual design to state‑of‑the‑art implementation.
• Curious, eager to dive into AI, physics, chemistry, and biology, and make foundational contributions.
• Strong cross‑functional collaboration skills with scientists across disciplines.
• No prior biology or chemistry experience is required, but curiosity and willingness to learn are essential.

Nice to Have

• Experience with LLMs, diffusion, reinforcement learning, or other cutting‑edge generative or predictive machine learning models.
• Experience in computational chemistry or drug discovery systems, especially protein‑ligand structure prediction, small‑molecule modeling, or computational drug discovery workflows.
• Generative modeling methods applied to scientific or molecular problems.
• LLM post‑training techniques such as 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).
• Advanced degree (MS or PhD) in machine learning, computer science, computational science, or equivalent research/engineering experience.

Benefits

• Competitive compensation package including salary and equity.
• Comprehensive health benefits: medical, dental, and vision (covered 100% for employees).
• 401(k) plan.
• Unlimited PTO policy.
• Free lunches and dinners at our offices.
• Paid family leave (maternity and paternity).
• Life, long‑term, and short‑term disability insurance.

Our Commitment to Equity

We are proud to be an inclusive workplace and an Equal Opportunity Employer.