AI/ML Engineer
We are building the foundational AI model and prompt-to-matter platform for chemistry-based products - an MCP/API layer that allows prompts and AI agents to design, formulate, and orchestrate robotic manufacturing.
Every formulation, batch, process parameter, and outcome becomes training data. Our manufacturing nodes are not just places where products are made - they are environments where the system learns how chemistry behaves in the real world. Connected through a unified intelligence layer, each run improves formulation design, process control, and production efficiency across the network.
When intelligence is embedded into the systems that transform matter, production becomes adaptive, local, and exponentially more capable. We are building a platform that will help power a radically abundant future.
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.
Interested in this role? Submit your application below and we will review it quickly.
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