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# Machine Learning Engineer, Physical AI (Modeling)

- **Company:** Samson Rose
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Computer-Aided Design, A/B Testing, Artificial Intelligence, Artificial Neural Networks, Computer Programming, Computer Graphics, Software Debugging, Machine Learning, Software Deployment, Reinforcement Learning, Transfer Learning, Model Validation, Information Technology, Optimization Algorithms, Computer-augmented Design and Manufacturing, Free and Open-Source Software, Data Pipelines, Data Generation
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-physical-ai-modeling-san-francisco-ca--72d99f43-7106-4b1d-9cbf-ee4e2d6ca3e9

## About the Role

Machine Learning, Artificial Intelligence (AI), CAD/CAM (Computer-Aided Design/Computer-Aided Manufacturing), Artificial Intelligence (AI), Startup, Product Demonstration, Assembly Line, Simulation, Fortune 500 Customers, Manufacturing, * 5+ years of hands-on industry experience training, debugging, and deploying machine learning models in production.

 * Experience building models that act in the physical world, such as robotics, robotic manipulation, autonomous vehicles, or other embodied AI systems.
 * Deep knowledge of modern neural network and foundation model architectures, optimization techniques, and hands-on experience with diffusion models and multimodal architectures.
 * Practical reinforcement learning experience with methods such as PPO, SAC, or GRPO, including reward modeling in production settings.
 * A strong understanding of model evaluation, including evaluation metrics, A/B testing, and offline and online evaluation frameworks.
 * The ability to read research papers, implement advanced techniques, and adapt them to solve real-world engineering challenges.
 * Strong programming and ML engineering skills, with the ability to take models from experimentation to reliable production deployment.
 * A BS, MS, or PhD in Computer Science, Robotics, Computer Graphics, or a related field, or equivalent practical experience.

Nice to Have

 * Experience with sim-to-real transfer, synthetic data generation, or co-training on simulated and real-world data.
 * Familiarity with vision-language-action (VLA) models, imitation learning, or manipulation policy learning.
 * Experience across the full ML stack, from data pipelines and training infrastructure to deployment and production monitoring.
 * Experience working on high-precision or contact-rich physical tasks, such as robotic assembly.
 * Open-source contributions or published research in machine learning, robotics, or embodied AI.
 * Experience at an early-stage startup or in a small team where engineers have significant ownership and move quickly.

## Description

This is a hands-on Machine Learning Engineering role focused on building the core models that power autonomous robotic assembly. You'll own the model lifecycle end to end, from defining data requirements and developing neural architectures to training, debugging, deploying, and validating models on real factory floors.

This isn't a research-only role or one focused on simply integrating existing models. The team is looking for an engineer who can translate cutting-edge ML research into production-ready systems, experiment with new approaches, and make independent technical decisions. You'll work across model architecture, reinforcement learning, synthetic data, evaluation, and sim-to-real transfer, with the autonomy to drive projects from initial ideas through deployment.

The team is small, fast-moving, and highly technical, offering significant ownership over the models and systems that will shape the future of manufacturing.