Research Scientist, Machine Learning

onepot; South San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Lunches and dinners in office

Job summary

onepot is automating chemistry and building a self-improvement loop by combining AI and robotics. We are seeking a machine learning researcher to train the next generation of chemistry models and enable synthesis of previously inaccessible molecules.

You will work across the full modeling stack with lab staff on data acquisition, train diverse models, and deploy them into experimental workflows. Strong ML fundamentals and curiosity about chemistry are essential, with ownership in a startup

Qualifications

• No formal education is required; ideal candidates have a deeply quantitative background or experience training ML models.
• Strong fundamentals in machine learning with an empirical/experimental focus.
• Ability to move quickly, make decisions with incomplete information, and solve difficult problems.
• Experience in startup or research environments with ownership and limited resources is relevant.

Responsibilities

• Train the next generation of chemistry models and enable synthesis of previously inaccessible molecules.
• Collaborate with lab staff on data acquisition and train models of varying shapes and sizes.
• Deploy models directly into experimental workflows and maintain ML training infrastructure and data pipelines.
• Develop abstractions for training diverse models, including parallelism strategies and quantization.
• Aim to train models with superhuman chemistry intuition and experimental capabilities.

Skills

ML fundamentals

Cross-disciplinary collaboration

Enthusiasm for full ML stack

Strong communication

Curiosity about chemistry

Tools

PyTorch

Job description

onepot is automating chemistry. Our goal is to enable a self‑improvement loop for chemistry by combining AI and advanced robotics. In this loop, AI systems design experiments, robotic systems execute them, and the resulting data improves the next generation of models.

This goal can only be achieved by bringing together people from different backgrounds: ML engineers, chemists, computer scientists, and hardware engineers. We are building a small, unusually ambitious team and are looking for a machine learning researcher to join us to help train the next generation of chemistry models.

We train models for a wide range of tasks, including reaction planning and outcome, input material costs, and mass spectra prediction. Most of these models are state‑of‑the‑art; many are trained on proprietary datasets that are larger, and higher quality, than what exists in the literature.

Our models are primarily deployed internally for real workflows. As such, we maintain a tight feedback loop between usage, data, and model training.

The role

In this position, you will train the next generation of chemistry models, and enable synthesis of previously inaccessible molecules.

Your work will span the full modeling stack. You will work with lab staff on data acquisition, train models of many different shapes and sizes, and deploy models directly into experimental workflows. You will build and help maintain infrastructure and abstractions for training a diverse set of models; this includes data pipelines and various system tasks such as parallelism strategies and quantization.

Ultimately, your role will be to train models with superhuman chemistry intuition and experimental capabilities.

Education

• No formal education is required. Ideal candidates will have some background in a deeply quantitative field, ideally with experience training ML models.

Experience

• Strong fundamentals in machine learning with a deep understanding of modern empirical/experimental ML
• Experience developing novel model training techniques or dealing with novel tasks or datasets
• Evidence that you can move quickly, make good decisions with incomplete information, and solve difficult problems without waiting for detailed instructions
• Experience in a startup, research group, competition team, or other environment where you had significant ownership and limited resources is particularly relevant

Skills

• Familiarity with PyTorch or other machine learning frameworks
• Knowledge of basic machine learning theory
• Enthusiasm about working across the entire machine learning stack (data, training, inference, deployment)
• Comfort working across disciplines and learning unfamiliar technical areas as necessary
• Strong written and verbal communication skills
• Curiosity and excitement about chemistry
• A strong bias toward building, testing, and learning from real systems

Particularly relevant experience

Experience in any of the following areas would be useful, but we do not expect one person to have all of it:

• Training LLM models (particularly mid- and post-training)
• Computer vision and embedded systems/robotics
• Machine learning systems (kernels, distributed training, etc.)
• Familiarity with chemistry models (retrosynthesis, mass spec modeling, etc.) or cheminformatics
• Active learning or other techniques suited for low-data regimes
• Scaling experiments and determining scaling laws

Who will thrive here

You may be a strong fit if you:

• Want to see your models used rather than benchmarked — here the loop closes in the lab, not on a leaderboard
• Are energized rather than discouraged by novel tasks with no established baseline or dataset
• Reach across the whole stack, from data acquisition through training to what runs in production
• Move with urgency while keeping enough rigor to know whether a result is real
• Want substantial responsibility early, including over what gets built and why
• Are willing to work outside a narrow job description to make the overall system succeed

Additional requirements

• Ability to work extended hours and weekends as necessary
• onepot works fully in person in our South San Francisco lab
• Ability to work safely in an active chemistry laboratory and around scientific equipment. This position does not involve lab work, but some projects may require an understanding of lab workflows.
• Lunches and dinners (if staying late) in office