Research Scientist, Machine Learning
$200,000–$250,000 year
On-siteSouth San Francisco, California, United States
South San Francisco, California, United StatesOn-siteFull Time$200,000–$250,000 yearDoctorate Or Professional DegreeStartup
Full TimeDoctorate Or Professional DegreeStartup
Job Summary
Train machine learning models for chemistry tasks including reaction planning, outcome prediction, and mass spectra analysis. Work with lab staff on data acquisition, build infrastructure for diverse model training, and deploy systems directly into experimental workflows. Develop novel training techniques and maintain abstractions for parallelism and quantization to enable synthesis of previously inaccessible molecules. Maintain a tight feedback loop between usage, data, and model training while working across disciplines.
Required Qualifications
- 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
- Ability to work extended hours and weekends as necessary
- Ability to work safely in an active chemistry laboratory and around scientific equipment
Desired Qualifications
- No formal education is required
- Ideal candidates will have some background in a deeply quantitative field, ideally with experience training ML models
- 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
- Experience in any of the following areas: 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
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