Principal Machine Learning Researcher (Physical AI) at Freeform

Los Angeles, CA (On-site)Full-timeMachine Learning & AIPosted 27 days ago

About the Role

<h2>PRINCIPAL MACHINE LEARNING RESEARCHER (PHYSICAL AI)</h2> Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts at the speed of human ideation. By treating manufacturing as a single integrated system, we unlock a new era of innovation where complex hardware is designed, built, and scaled without limits. This architecture enables continuous generation of petabyte-scale, high-fidelity data capturing the physics of metal printing - from in-situ process signals and machine state to geometry and material outcomes. Each factory node contributes to a growing learning system that improves modeling accuracy, control performance, yield, and scalability over time.  Freeform is hiring a Principal Machine Learning Researcher to lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system. The role focuses on developing machine learning methods that integrate large-scale physical data with physics-based simulation and embedding these models into closed-loop control and autonomy frameworks. Work includes modeling relationships between process inputs, geometry, and machine state to predict thermal, mechanical, and geometric outcomes during printing, using hybrid physics–ML approaches and multi-modal in-situ data.  Research is validated against physical outcomes and deployed into production systems, where improvements directly impact stability, yield, throughput, and capability across an expanding fleet of manufacturing nodes. Your work will have a direct and meaningful impact on how frontier technologies are designed and produced at scale.  <iframe style="width: 560px; height: 315px;" src="https://www.youtube.com/embed/sXfG6ssZ190?feature=shared" width="327" height="184"></iframe> Responsibilities: <ul> <li>Design and develop machine learning models for complex, multi-physics manufacturing processes. </li> <li>Develop hybrid modeling approaches that combine first-principles physics with data-driven learning. </li> <li>Lead the formulation of learning-based models used for prediction and control in production-scale metal additive manufacturing systems. </li> <li>Develop methods to learn from large-scale, high-dimensional in-situ sensor data collected during printing. </li> <li>Design unsupervised and self-supervised learning techniques to correlate process signals with part quality, geometry, and performance. </li> <li>Develop models that link process parameters, geometry, and machine state to thermal and mechanical outcomes. </li> <li>Integrate learned models with physics-based simulation and digital twin frameworks. </li> <li>Contribute to the design of closed-loop control and autonomy systems that operate in real time on production hardware. </li> <li>Develop learning-based approaches for machine health monitoring, anomaly detection, and system diagnostics. </li> <li>Guide the integration of machine learning models into production software and manufacturing workflows. </li> <li>Help define research direction and technical standards for machine learning applied to physical systems within the organization. </li> </ul> Basic Qualifications: <ul> <li>5+ years of experience in machine learning, applied research, or related technical fields or a PhD in machine learning, applied mathematics, physics, robotics, controls, or a closely related discipline. </li> <li>Strong foundations in machine learning applied to physical systems, modeling, or control. </li> <li>Proficiency in Python and at least one systems-level programming language (C/C++ preferred). </li> <li>Experience working with large-scale, noisy, real-world datasets. </li> </ul> Nice to Have: <ul> <li>MS or PhD in applied mathematics, physics, robotics, controls, materials science, or a related discipline. </li> <li>Experience with hybrid physics–ML models, digital twins, or simulation-in-the-loop learning. </li> <li>Background in autonomy, robotics, model predictive control, or reinforcement learning for physical systems. </li> <li>Experience with image-based or sensor-based inference in industrial or scientific settings. </li> <li>Familiarity with computational geometry or geometric modeling. </li> <li>Comfort working across theory, experimentation, and deployment in tightly coupled systems. </li> <li>Ability to reason from first principles and translate theory into working models and systems. </li> </ul> Location: <ul> <li> Based in Hawthorne, our vertically integrated facility brings technology development, R&D, and production together under one roof. We operate at the center of LA’s deep tech ecosystem, surrounded by some of the most ambitious hardware innovation happening anywhere in the country. <span id="message-body-1771454273221" class="fui-ChatMessage__body r7802u9 ___vfhe6q0 f10pi13n ftqa4ok f2hkw1w f8hki3x f1d2448m f1bjia2o ffh67wi f1j6vpng f1pniga2 f987i1v f1ffjurs f15bsgw9 f14e48fq f18yb2kv fd6o370 ffwy5si f3znvyf f57olzd f4stah7 f480a47 fs1por5 fk6fouc figsok6 fkhj508 f19n0e5 f9ijwd5 ffzz00n f1ozlkrg f1o0qvyv f9ggezi f1xp5gbu f150uoa4 ffyari3 f16xq7d1 fo7qwa0 fxowb0n f11ghf3q f13aoclr flypziy f10kwr27 fquw1qa fftr39l f13lathq f15hsm81 f2ss68y ffb60jq f8nuap2 f13nk4fk f7jacry fq08z5q fd9af6s fr74w9q fcl9uv6 f13sm7pj f1u6qqly f16wpxbl faim3u9 f6cs3qo fa2w2z3 fd39nx6 f10gn8j9 frcqmxy f1w9ws4k f1ddxkqj fd10euv fvuz61 f1nbc6gw"></li> <li> Our fast-paced, cross-functional environment is built on close collaboration, and as such, this role requires full-time onsite presence (five days a week), with very limited exceptions. </li> </ul> What We Offer: <ul> <li>We have an inclusive and diverse culture that values collaboration, learning, and making deliberate data-driven decisions.</li> <li>We offer a unique opportunity to be an early and integral member of a rapidly growing company that is scaling a world-changing technology.</li> <li>Benefits <ul> <li>Significant stock option packages</li> <li>100% employer-paid Medical, Dental, and Vision insurance (premium PPO and HMO options)</li> <li>Life insurance</li> <li>Traditional and Roth 401(k)</li> <li>Relocation assistance provided</li> <li>Paid vacation, sick leave, and company holidays</li> <li>Generous Paid Parental Leave and extended transition back to work for the birthing parent</li> <li>Free daily catered lunch and dinner, and fully stocked kitchenette</li> <li>Casual dress, flexible work hours, and regular catered team building events</li> </ul> </li> <li>Compensation   <ul> <li>As a growing company, the salary range is intentionally wide as we determine the most appropriate package for each individual taking into consideration years of experience, educational background, and unique skills and abilities as demonstrated throughout the interview process. Our intent is to offer a salary that is commensurate for the company’s current stage of development and allows the employee to grow and develop within a role. </li> <li>In addition to the significant stock option package, the estimated salary range for this role is $200,000-$400,000. However is this a unique position with outsized impact for the right game-changing hire, so we will consider compensation outside of this range on a case-by-case basis.</li> </ul> </li> <li>Freeform is an Equal Opportunity Employer that values diversity; employment with Freeform is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.</li> </ul>

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