About the Role
As an AI/ML Engineer, you'll build the intelligence layer that powers our prompt-to-matter platform. Your work will sit at the core of a closed-loop manufacturing system where real-world production data feeds learning systems that continuously improve formulation, process control, and product outcomes.
We are embedding outcome-driven AI directly into chemical manufacturing - turning factories into compounding systems and throughput into learning. You will work on reinforcement learning, optimization systems, predictive modeling, and data pipelines that connect physical production to cloud-based intelligence.
This is not a research-only role. You will ship production systems that interact with robotics, sensors, process controls, and distributed manufacturing nodes operating in the real world.
This is a full-time role based in California, with in-person collaboration expected in our Mountain View office.
What You'll Do
- Design and deploy machine learning systems that optimize manufacturing processes and formulation performance.
- Build reinforcement learning or optimization loops for process control and parameter tuning.
- Develop models that connect formulation inputs to measurable real-world outcomes.
- Work with robotics and controls engineers to integrate ML systems into physical production environments.
- Design data pipelines for ingesting telemetry, quality signals, and experimental results.
- Run controlled experiments to improve yield, consistency, and product performance.
- Translate ambiguous business goals into measurable optimization objectives.
- Collaborate with Product and Engineering to deploy ML systems safely and reliably.
- Continuously improve model performance using real-world feedback loops.
What We're Looking For
- 3+ years of experience building and deploying ML systems in production environments.
- Strong foundation in statistics, optimization, and machine learning fundamentals.
- Experience with reinforcement learning, Bayesian optimization, control systems, or experimental design is highly preferred.
- Strong programming skills (Python preferred) and familiarity with modern ML frameworks.
- Comfortable working with messy real-world data from physical systems.
- Ability to balance research ambition with practical shipping constraints.
- High agency and comfort operating in ambiguous, fast-moving environments.
- Systems thinker -- you understand how models interact with hardware, latency, safety, and operational constraints.
- Actively leverages modern AI tools to accelerate experimentation, iteration, and deployment.
- Nice-to-haves: manufacturing data, chemical/process engineering exposure, control theory, distributed systems.
What We Offer
- Competitive compensation with equity.
- Ownership over foundational intelligence infrastructure powering physical production.
- Opportunity to work at the intersection of AI, robotics, and real-world manufacturing.
- Direct impact on systems that operate globally and improve with every production cycle.
- High-autonomy environment built for ambitious, fast-learning builders.