Senior Simulation Engineer

Freeform

PRINCIPAL MACHINE LEARNING RESEARCHER (PHYSICAL AI) 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.  Responsibilities: * Design and develop machine learning models for complex, multi-physics manufacturing processes.  * Develop hybrid modeling approaches that combine first-principles physics with data-driven learning.  * Lead the formulation of learning-based models used for prediction and control in production-scale metal additive manufacturing systems.  * Develop methods to learn from large-scale, high-dimensional in-situ sensor data collected during printing.  * Design unsupervised and self-supervised learning techniques to correlate process signals with part quality, geometry, and performance.  * Develop models that link process parameters, geometry, and machine state to thermal and mechanical outcomes.  * Integrate learned models with physics-based simulation and digital twin frameworks.  * Contribute to the design of closed-loop control and autonomy systems that operate in real time on production hardware.  * Develop learning-based approaches for machine health monitoring, anomaly detection, and system diagnostics.  * Guide the integration of machine learning models into production software and manufacturing workflows.  * Help define research direction and technical standards for machine learning applied to physical systems within the organization.  Basic Qualifications: * 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.  * Strong foundations in machine learning applied to physical systems, modeling, or control.  * Proficiency in Python and at least one systems-level programming language (C/C++ preferred).  * Experience working with large-scale, noisy, real-world datasets.  Nice to Have: * MS or PhD in applied mathematics, physics, robotics, controls, materials science, or a related discipline.  * Experience with hybrid physics–ML models, digital twins, or simulation-in-the-loop learning.  * Background in autonomy, robotics, model predictive control, or reinforcement learning for physical systems.  * Experience with image-based or sensor-based inference in industrial or scientific settings.  * Familiarity with computational geometry or geometric modeling.  * Comfort working across theory, experimentation, and deployment in tightly coupled systems.  * Ability to reason from first principles and translate theory into working models and systems.  Location: * 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. * 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. What We Offer: * We have an inclusive and diverse culture that values collaboration, learning, and making deliberate data-driven decisions. * We offer a unique opportunity to be an early and integral member of a rapidly growing company that is scaling a world-changing technology. * Benefits * Significant stock option packages * 100% employer-paid Medical, Dental, and Vision insurance (premium PPO and HMO options) * Life insurance * Traditional and Roth 401(k) * Relocation assistance provided * Paid vacation, sick leave, and company holidays * Generous Paid Parental Leave and extended transition back to work for the birthing parent * Free daily catered lunch and dinner, and fully stocked kitchenette * Casual dress, flexible work hours, and regular catered team building events * Compensation   * 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.  * 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. * 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.

AUTOLIV SVERIGE AKTIEBOLAG

Autoliv is the worldwide leader in automotive safety systems. We develop, manufacture and market protective solutions such as airbags, seatbelts, and steering wheels for all major automotive manufacturers worldwide, along with advanced safety solutions for new mobility. In 2025, our products helped save approximately 40,000 lives and prevented around 600,000 serious injuries. We are now looking for an experienced Simulation Engineer to lead the development and operation of a high-fidelity virtual validation platform for next-generation interior and occupant safety systems. In this role, you will play a key part in accelerating product development and delivering release-grade safety evidence through advanced simulation methodologies Role and Responsibilities Design and maintain end-to-end XiL environments (SiL, HiL, DiL/ViL) for safety-critical functions. Integrate vECUs (AUTOSAR Classic/Adaptive), environment models, and CI/CD pipelines. Develop and execute scenario-based validation, including edge cases and misuse scenarios. Generate synthetic scenarios using tools such as NVIDIA Omniverse, CARLA, and Human Body Models (HBM). Validate and correlate simulation models against physical test data. Automate simulation execution, regression testing, evidence generation, and reporting. Support functional safety activities and maintain traceability in accordance with ISO 26262 and SOTIF. What You’ll Bring Bachelor's or master’s degree in engineering or a related field. 8+ years of experience in automotive simulation, ADAS/active safety validation, vehicle dynamics, or XiL environments. Strong programming skills in Python, C/C++, and experience with Git, Jenkins, and Docker. Hands-on experience with MATLAB/Simulink, dSPACE, and scenario-based testing. Knowledge of AUTOSAR, CAN/CAN-FD, Automotive Ethernet, and CANalyzer. Experience with simulation automation, virtual ECUs, and safety-critical development environments. Fluent verbal and written communication skills in English. Who Are You? You are analytical, self-driven, and passionate about solving complex technical challenges. You thrive in cross-functional teams, communicate effectively, and consistently deliver high-quality results. You bring: Strong analytical mindset and attention to detail Excellent problem-solving capabilities Ability to communicate complex technical information clearly, both verbally and in writing Self-driven with strong time-management skills Effective collaborator in cross-functional and international teams Adaptable and open to new technologies and changing priorities Structured, rigorous, and quality-focused approach to engineering Why Autoliv? At Autoliv, our purpose is simple: Saving More Lives. By joining our team, you will have the opportunity to influence the next generation of safety innovations while working alongside some of the industry's most talented experts. Together, we develop technologies that make a real difference for people around the world. We would love to hear more about your experience, ambitions, and what inspires you. Let's start the conversation. Your Application Does this sound like the opportunity you’ve been waiting for? Please apply and feel free to contact us if you have any questions. Practical information Place of Work: Vårgårda (Hybrid) Scope: Full-time Employment type: Permanent Start date: According to agreement  More lives saved – more life lived!

PhysicsX

ABOUT US PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. NOTE: WE ARE CURRENTLY RECRUITING FOR MULTIPLE POSITIONS, HOWEVER PLEASE ONLY APPLY FOR THE ROLE THAT BEST ALIGNS WITH YOUR SKILLSET AND CAREER GOALS. THE ROLE The Senior Simulation Data Engineer will extend and operate the infrastructure that powers our research Data Factory. You will be responsible for the end-to-end pipeline: from geometry preparation and simulation orchestration through validation, post-processing, and delivery to downstream ML training systems, using PhysicsX platform orchestration services where synergies exist. This role sits at the intersection of HPC engineering and data engineering. You will orchestrate long-running CFD simulations at scale, build robust data pipelines, and ensure that every simulation we produce meets rigorous quality standards. TEAM CONTEXT In this role, you will be vertically embedded in Research , working daily with: * Research Scientists who define data requirements and quality standards * ML Engineers who consume Data Factory outputs for model training * ML Infrastructure Engineers who are accountable for downstream training infrastructure You will have end-to-end responsibilities over the Data Factory, with the autonomy to make architectural decisions and the responsibility to keep data flowing reliably. Horizontally, you will be part of an infrastructure engineering group responsible for infrastructure across the company. WHAT YOU WILL DO SIMULATION ORCHESTRATION * Extend and operate the Data Factory infrastructure that orchestrates thousands of CFD simulations per day on cloud compute * Design and operate job scheduling systems that maximize throughput while handling failures gracefully * Build monitoring and alerting to detect simulation failures, convergence issues, and resource bottlenecks early DATA PIPELINE ENGINEERING * Build high-performance data pipelines that move simulation outputs from solver results to ML-ready training data * Implement geometry preprocessing workflows (mesh preparation, morphing, watertightness validation) * Design and operate post-processing pipelines: surface decimation, field interpolation, format conversion * Optimize I/O performance for large mesh datasets DATA QUALITY AND VALIDATION * Implement comprehensive validation checks at every pipeline stage: solver convergence, physical field bounds, post-processing fidelity * Build systems that capture and quarantine bad data before they reach training pipelines * Track and report data quality metrics across the entire Data Factory * Work towards full provenance: training samples should be traceable back to their source geometry and simulation configuration INTEGRATION AND DELIVERY * Deliver validated datasets to downstream ML training infrastructure in formats optimized for efficient data loading * Design data versioning and cataloging systems that support reproducible training runs * Work closely with ML Infrastructure Engineers to ensure smooth handoff between data production and model training * Support multi-dataset training workflows WHAT YOU BRING TO THE TABLE * Ability to scope and effectively deliver projects, prioritising activity as needed. * Problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. * Excellent collaboration and communication skills, especially in a research setting. You can translate "the model isn't converging" into infrastructure hypotheses and solutions, and can bridge technical abstractions with implementations. * 5+ years of experience in data engineering, HPC engineering, or simulation infrastructure. * Strong experience with orchestration systems: SLURM, Kubernetes, Temporal * Production data pipeline experience: you've built and operated pipelines that process large volumes of data reliably * Proficiency in Python for pipeline development and automation * Systems engineering fundamentals: Linux, networking, storage systems, performance debugging * Experience with cloud infrastructure; ****ideally CoreWeave or similar GPU/HPC-focused clouds * Background in HPC for simulation engineering: experience with CFD, FEA, or similar computational workflows (StarCCM+, OpenFOAM, ANSYS, etc.) * Experience with geometry processing: mesh manipulation, CAD formats, PyVista * Familiarity with scientific data formats: HDF5, VTK, NetCDF, Zarr * Data quality engineering experience: validation frameworks, anomaly detection, data observability IDEALLY * Understanding of CFD fundamentals, enough to interpret solver outputs and validation metrics * Experience with 3D geometry pipelines (mesh decimation, field interpolation) * Familiarity with ML data loading patterns and how training systems consume data What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 🏦 10% employer pension contribution - because investing in future matters. 🍽️ Free office lunches - to keep you energised and focused. 👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. 🍼 YellowNest nursery scheme - to help working parents manage childcare costs. ☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind. 💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing. 👀 Eye tests - because good work depends on good health. 📈 Personal development - dedicated support for learning, development, and leveling up over time. 💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it. 🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener. 🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 🔎 Watch this space, we’re continuing to build this as we grow…   We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.    We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.