We’re supporting a client with the recruitment requirement outlined below. If your recruiting expertise and networks align with the role, please apply to express your interest.

Once approved to work on the opportunity, you’ll receive the full brief, including the client name, role details, and commercial terms.

Role Details

Location: San Francisco, California

Industry: AI / Scientific AI

Salary: $200,000–$300,000

Equity: Meaningful equity

Employment Type: Full-time

Work Arrangement: In-office

Experience: Typically 5+ years, including doctoral research

Visa Sponsorship: Yes; transfers and renewals may be supported

About the Company

An AI-native chemical manufacturing company developing a conversational chemistry platform that uses AI to plan synthesis routes, engineer processes, and learn from laboratory outcomes.

The platform combines AI models, laboratory capabilities, and a manufacturing network to support enterprise customers. The company is building an AI research team in the San Francisco Bay Area, focused on applying advanced machine learning to real-world scientific and industrial challenges.

About the Role

The AI Researcher will help develop models that translate scientific knowledge and incomplete laboratory observations into reliable decisions about chemical synthesis and manufacturing processes.

The central challenge is building a robust learning loop between model predictions and experimental outcomes. This involves selecting and adapting models, developing training and post-training methods, designing evaluations, and connecting offline model improvements to actual laboratory performance.

The role spans research, model training, evaluation, and deployment, with close collaboration across AI engineering, product, and chemistry teams.

Responsibilities

• Build and adapt model-training pipelines, including continued pretraining, supervised fine-tuning, data mixtures, molecular representations, and chemistry-specific objectives.

• Develop reinforcement learning approaches using experimental feedback, reaction outcomes, yields, and process constraints.

• Build synthetic-data, expert-trace, and distillation pipelines with appropriate data provenance and quality controls.

• Develop benchmarks for synthesis planning, procedure generation, and tool use.

• Evaluate model generalization to unfamiliar chemistry and connect offline evaluation results to laboratory performance.

• Own distributed training runs, troubleshoot instability, and improve training throughput and checkpointing.

• Explore reaction and process models for outcome prediction, search, and planning.

• Work directly with chemists and product engineers to prioritize research problems, analyze failures, and improve models.

• Contribute to research direction, engineering standards, mentoring, publications, or open-source work where appropriate.

Requirements

• Strong track record of training or post-training large models, with ownership of substantive experiments.

• Deep expertise in at least one of the following:

• Reinforcement learning and reasoning.

• Large language model training.

• Synthetic data and model distillation.

• World models and model-based planning.

• Strong research programming skills in Python and PyTorch or JAX.

• Hands-on experience with distributed training systems such as FSDP, DeepSpeed, or Megatron.

• Ability to design reproducible evaluations and investigate data leakage, reward exploitation, and distribution shift.

• Experience taking ambiguous research problems through to dependable models used in practice.

• Ability to own work from research hypotheses through training, evaluation, and deployment.

• Strong communication and collaboration skills, particularly when working with domain experts.

Benefits

• Health, medical, and dental coverage.

• Meaningful equity.

Interview Process

• Founder Screen

• Technical Interview

• Research & Collaboration Interview

• Reference Screening

• Offer and Hire.