AI/ML Engineer

Formulate Robotics; Mountain View (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Equity

Impactful work

Autonomy

Cross-functional collaboration

Job summary

Formulate Robotics in California is seeking an AI/ML Engineer to build the intelligence layer powering our prompt-to-matter platform. You will ship production systems that connect robotic and process controls with cloud-based learning, driving real-world improvements in formulation and manufacturing.

You will design ML models, reinforcement learning loops, data pipelines, and experiments, collaborating with robotics, controls, and software teams.

Qualifications

• 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 who understands how models interact with hardware, latency, safety, and constraints.
• Actively leverages modern AI tools to accelerate experimentation, iteration, and deployment.

Responsibilities

• 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.

Skills

ML in production

Python

Statistics

Optimization

Reinforcement learning

Bayesian optimization

Control systems

Experiment design

Data from physical systems

Tools

TensorFlow

PyTorch

scikit-learn

Job description

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.

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.