Principal Machine Learning Researcher (Physical AI)

Freeform

PRINCIPAL SIMULATION ENGINEER  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. As a Simulation Engineer at Freeform, you will be responsible for developing physics-based, data-driven models that enable the first production scale, high quality, and fully automated metal 3D printing factory capability. We are looking for someone with deep knowledge in physical simulations and multi-physics phenomena who can develop sophisticated models from scratch for undefined or previously unsolved problem statements. This person needs to be comfortable working in an interdisciplinary environment that iterates quickly and decisively. 3D printing experience is not required to be successful here - rather we look for smart, motivated, collaborative engineers who love solving hard problems and creating amazing technology! Responsibilities:  * Develop high-performance numerical solvers for simulating the physics of advanced metal 3D printing systems * Implement numerical simulation models for real-time or near-real-time performance on HPC or GPU platforms * Work with software engineers to build and deploy physics-based and data-driven models of the laser powder bed fusion process * Work with software engineers to integrate low-level solvers into simulation pipelines and interface with hardware systems * Guide the integration of computational geometry and meshing techniques to support large-scale, physics-driven simulations * Architect simulation modules for execution on specialized compute hardware including FPGAs and GPUs * Contribute to the development of model-predictive control strategies alongside control engineers * Generate and maintain documentation for all algorithms, solvers, and integrated system Basic Qualifications: * Bachelor’s degree in applied mathematics, physics, computer science, mechanical engineering, or a related technical field * 10+ years of professional experience developing custom simulation solvers with a PhD in one of the above fields * Demonstrated experience writing low-level numerical solvers for physical simulations * Strong programming skills in C, C++, or CUDA * Experience developing simulations in a high-performance compute environment (i.e. running on GPUs) * Academic or professional familiarity with solid mechanics, continuum mechanics, or other physical modeling domains Nice to Have: * Experience with real-time or near-real-time simulations on GPU or parallel computing platforms * Experience with direct numerical simulation (DNS) models * Familiarity with solid mechanics, continuum mechanics, or other physical modeling domains * Experience with thermo-elastic or thermo-elastoplastic modeling * Familiarity with computational geometry and meshing techniques * Experience with data-driven modeling techniques and hybrid physics/data simulation methods * Familiarity with optimization techniques and control theory * Experience with 2D and 3D meshing libraries and tools * Creative problem solver with a strong foundation in first-principles thinking and numerical analysis * Strong written and verbal communication skills 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 $225,000-$300,000. 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.

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 Principal ML Infrastructure Engineer will extend and operate the infrastructure that powers our research model training, fine-tuning, and serving pipelines. You will be embedded within our Research function, partnering directly with ML engineers and research scientists to ensure they can train Large Physics Models efficiently and reliably at scale. TEAM CONTEXT In this role, you will be vertically embedded in Research, working daily with: * Research Scientists who determine the model architectures and methods * ML Engineers who implement and develop the models * Simulation Data Engineers who are accountable for upstream data pipelines You will have end-to-end responsibilities over the research infrastructure, 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 TRAINING INFRASTRUCTURE * Design and operate distributed training infrastructure for neural operator architectures (Transolver, Point Cloud Transformer, etc.) on our large NVIDIA DGX B200 platform. * Optimize training pipelines for throughput, fault tolerance, and cost efficiency, including checkpointing strategies, gradient accumulation, and multi-node synchronization. * Build and maintain experiment tracking and observability systems that give researchers clear visibility into training runs, hyperparameter sweeps, and model performance. DATA I/O AND PERFORMANCE * Solve data loading bottlenecks for large-scale mesh datasets. * Optimize data pipelines for efficient I/O from cloud storage, including prefetching, caching, and format optimization. * Work with heterogeneous data sources of varying formats and resolutions. MODEL SERVING AND DEPLOYMENT * Build serving infrastructure for pre-trained LPMs, supporting both zero-shot inference and uncertainty quantification (Monte Carlo Dropout). * Design and implement model packaging pipelines for customer deployment. Models must run reliably in customer environments with fine-tuning capabilities. * Ensure reproducibility: any model checkpoint should be deployable with consistent behaviour. PLATFORM AND TOOLING * Improve developer experience for the Research team with fast iteration cycles, reliable CI/CD, clear debugging tools. * Collaborate with the broader Infrastructure team on shared patterns and standards. 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 building and operating ML infrastructure at scale: * Deep expertise in distributed training: you've debugged NCCL hangs, optimized collective communication, and know when to use FSDP vs. DDP vs. pipeline parallelism * Strong systems fundamentals: Linux, networking (including domain specific NVLink and InfiniBand), storage I/O, profiling and performance optimization * Production experience with Kubernetes and SLURM for job orchestration on GPU clusters * Proficiency in Python and ML frameworks (PyTorch strongly preferred) * Experience with cloud GPU infrastructure; ideally CoreWeave or similar GPU/HPC-focused clouds IDEALLY * Experience with geometric deep learning or neural operators, ****architectures that operate on meshes, point clouds, or graphs * Background in HPC for simulation engineering, familiarity with how CFD/FEA workflows generate and consume data * Experience building model serving infrastructure with latency and throughput requirements * Familiarity with experiment tracking tools (Weights & Biases, MLflow) and observability stacks (Prometheus, Grafana) * Experience packaging models for deployment into customer environments (containers, model registries, versioning)   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.

Databricks

PRINCIPAL RESEARCH SCIENTIST – SCALING P-1227 ABOUT DATABRICKS AI At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development, by building and running the world’s best data and AI platform. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all. ABOUT THE SCALING RESEARCH TEAM The Databricks AI Scaling team focuses on pushing the boundaries of large language model (LLM) training and inference efficiency beyond what is required to support existing models. The team explores novel avenues for scaling and efficiency improvements across algorithms, systems, and infrastructure, requiring researchers who can both drive independent research agendas and dive deep into low‑level implementation details with engineering partners. ROLE SUMMARY As a Principal Research Scientist – Scaling, you will lead a team of world‑class researchers and engineers to advance the state of the art in large‑scale machine learning, focusing on post-training, RL and inference efficiency, optimization, and scaling. You will define and execute a research roadmap that advances the Databricks AI platform and delivers tangible improvements to how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders to bring cutting‑edge methods into production. THE IMPACT YOU WILL HAVE * Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with a particular emphasis on LLM scaling, efficiency, and systems performance. * Define the scaling research roadmap in alignment with Databricks’ strategic objectives, prioritizing advances in foundation model efficiency and large‑scale training and inference. * Drive algorithmic innovations for large‑scale neural network training and inference, including novel optimizers, low‑precision techniques, and model adaptation methods, and guide your team in rigorous empirical validation against state‑of‑the‑art approaches. * Optimize end‑to‑end ML systems for distributed training and RL, memory efficiency, and compute efficiency through close collaboration with core systems and platform teams, ensuring that research ideas translate into performant, reliable infrastructure. * Partner with product and engineering to translate research breakthroughs, especially around scaling and efficiency, into customer‑impacting capabilities in the Databricks AI platform. * Foster a culture of scientific excellence and openness, including high‑quality research practices, reproducible experimentation, and effective internal knowledge sharing across Databricks AI. * Represent Databricks AI research externally through top‑tier publications, conference talks, and collaborations with academia and the open‑source community, with a focus on optimization and efficiency for large‑scale models. * Mentor and develop talent, providing both technical guidance (research agendas, experimentation, implementation) and career development support for research scientists and engineers. WHAT YOU WILL DO * Define and lead independent research programs on foundation model efficiency, covering topics such as optimizer design, low‑precision training/inference, scalable model architectures, and efficient adaptation methods. * Oversee the design and execution of large‑scale experiments, including benchmarking against state‑of‑the‑art methods and evaluating trade‑offs in quality, latency, throughput, and cost. * Work hands‑on with your team on high‑quality, efficient code in Python and PyTorch for research implementation, rapid prototyping, and integration with Databricks’ production systems. * Collaborate with distributed systems and infra teams to push the limits of distributed training, parallelism strategies, memory management, and hardware utilization for LLMs and other large models. * Establish metrics, evaluation protocols, and best practices for scaling‑focused research (e.g., training efficiency, inference cost, energy usage) and drive their adoption across Databricks AI. * Champion responsible and robust deployment of scaling innovations, ensuring that model behavior, reliability, and safety remain first‑class considerations. WHAT WE LOOK FOR * Proven ability to lead a research team to develop novel techniques for foundation model efficiency and related topics, with a strong track record of industry impact.  * Deep expertise in at least one of: generative AI, LLMs, distributed ML systems, model optimization, or responsible AI, with a strong emphasis on scaling and efficiency for large‑scale neural networks. * Hands on leadership - strong programming skills and demonstrated ability to write high‑quality, efficient code in Python and PyTorch for research implementation and experimentation. * Demonstrated ability to translate research innovation into scalable product capabilities in partnership with product and engineering teams. * Excellent communication, leadership, and stakeholder management skills, with experience influencing cross‑functional roadmaps and aligning research with business impact. NICE TO HAVE * Prior work at the intersection of systems and ML, such as distributed training frameworks, compiler and kernel optimization for deep learning workloads, or memory‑/compute‑efficient model design. * Strong industry and academic network in large‑scale ML, with ongoing collaborations or service (e.g., PC/area chair) at top conferences in ML and systems. * A strong record of research impact—such as first‑author publications at top ML/systems conferences (e.g., ICLR, ICML, NeurIPS, MLSys), influential open‑source contributions, or widely used deployed systems—especially in optimization or efficiency.   Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.   Local Pay Range $280,000—$350,000 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here. Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.