AI / Machine Learning Researcher

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.

Extend

Extend is building a modern document processing cloud. We're on a mission to transform how the world works with unstructured data. As a Machine Learning Engineer at Extend, you’ll be responsible for building state-of-the-art document processing infrastructure. The work you do will impact every single customer across the millions of documents that our system ingests and processes every month. We’re looking to hire engineers who think creatively about problems, keep up to date on the latest in AI research, push for real-world customer impact (not only theory), and are excited about helping businesses tackle one of their most painful problems with AI. WHY YOU SHOULD CONSIDER JOINING * We've grown revenue 400% last year to several million ARR, with our growth continuing to accelerate * We have a product loved by users and being used in mission-critical flows at enterprises (Square, Zillow, Chime) and startups (Brex, Mercury, Checkr), and many more * The market for document processing has expanded 1000x due to LLMs, and all existing solutions are low NPS * We're punching well above our weight, supporting customer and revenue metrics with half the team size of other startups — everyone joining at this stage will have outsized impact * We've raised our series A & de-risked the business, but there's tremendous upside ahead; now's the perfect time to join. * You'll be joining a talent dense team (e.g. former founders, world record holders) operating in a high performance culture, in-person in NYC, with high equity ownership * We're tackling novel, unsolved problems at the intersection of LLMs, complex data processing, and human ↔ AI interfaces * You'll get a front row seat to how a successful startup is built with plenty of leadership opportunities as we grow quickly We're a lean team, and our customer base is growing too fast for us to keep up. We're looking for talented engineers to join our team and execute on an ambitious roadmap. ROLE * Training and deploying SOTA vision models for document processing * Design novel LLM techniques for increasing the complexity of use cases and data streams that Extend can be applied to * Monitor existing models in production, understand what’s working well (and what isn’t), and run experiments to solve those issues * Have complete ownership over the work you do — as a founding team member, you’ll have the opportunity to truly own large areas of product and engineering and have direct relationship with customers. * Work directly with the CEO, CTO, and other founding members and help build out our team. BENEFITS Beyond the below, we care deeply about each of our candidates on a personal level. We take the time to understand your needs to craft a package designed for you: * Insurance - 90% health insurance premium coverage * Relocation - for candidates based outside of NYC * Unlimited PTO policy - we trust everyone like owners, take what you need * Daily lunch & snacks covered (plus dinner if you're in the office late) - you'll never go hungry and you help pick the snacks * Unlimited token / tooling access - no handicaps on your productivity * Learning and development investment - we invest heavily in our team and support your growth Extend is an Equal Opportunity Employer. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, citizenship status, marital status, pregnancy, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.

CHALMERS TEKNISKA HÖGSKOLA AKTIEBOLAG

We are seeking an ambitious applied researcher who wants to work at the forefront of data-driven nutrition and health. This is a unique opportunity to bridge mathematics, statistics, AI, and biomedical research in a highly interdisciplinary research environment. About us Fraunhofer-Chalmers Centre (FCC) is a leading research centre in industrial mathematics, modelling, simulation, optimisation, and data analytics. We operate at the interface between academic research and industrial needs, often in interdisciplinary projects where mathematics, data, and domain expertise meet. Together with the nutrition research group led by Professor Rikard Landberg at Chalmers University of Technology, we conduct advanced research in data-driven nutrition, health, and food science. With large-scale diet and health data, omics data, biomarkers, digital food and health services, we establish predictive models for evaluation of the role of diet in health and disease and establish personalized dietary strategies for more effective disease prevention. In many cases, the work involves time series data, dynamic processes and phenomena, where both methods and interpretation must account for temporal dynamics. Data typically comes from intervention studies conducted in Gothenburg and or from large cohorts and biobanks from international collaborators. To secure and further develop this important collaboration, we are now recruiting an applied researcher with a strong quantitative profile and interest in nutrition, health, or medicine. Your role As an applied researcher in this area, you will: Act as a key person in the collaboration between FCC and the nutrition research group at Chalmers. Lead and conduct research projects in data-driven nutrition, such as:analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive dynamical models and digital decision-support tools for nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor data such as CGM measurements (continuous glucose monitoring), activity trackers, and other wearable sensors. Serve as a bridge between domain researchers (nutrition, medicine, food science) and quantitative experts (mathematics, statistics, AI&ML, systems engineering). Supervise, collaborate, and support PhD students and postdocs involved in joint projects. Your profile We are looking for someone who: Holds a PhD in Applied Mathematics, Mathematical Statistics, Automatic Control, Signal Processing, Systems Engineering, Data Science, or a related field. Has experience in data-driven research, preferably related to biomedicine, nutrition, epidemiology, food science, or public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible analysis workflows. Enjoys working in interdisciplinary environments and is curious about understanding biological/nutritional research questions in depth. It is meritorious if you also: Have experience working with cohort data, registry data, clinical studies, or omics data. Have previously worked in projects involving both academic and industrial/external partners. Have experience supervising PhD students, postdocs, or junior researchers. Can communicate in both Swedish and English; excellent English is required. What we offer With us, you will: Have a unique opportunity to combine advanced quantitative and data-driven methods with cutting-edge research in biomedicine, nutrition, and health. Work in a strong and long-term collaboration between Fraunhofer-Chalmers Centre and Chalmers division of Food and Nutrition Science, with access to both international academic and industrial networks. Influence the direction of a strategically important initiative in data-driven nutrition and health. Be part of a creative, collaborative, and international research environment with good opportunities for personal and scientific development. Employer: Fraunhofer-Chalmers Research Centre for Industrial Mathematics (Fraunhofer-Chalmers Centre, FCC)   Employment: according to agreement   Location: Gothenburg, Sweden   Department: Systems and Data Analysis (in close collaboration with Chalmers)   Application procedure The application should be written in English and attached as PDF-files, containing as specified below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files.  - CV  - Publication list - Transcript of grades - Personal letter with: A brief description of your research profile and how it relates to nutrition/health A brief motivation as to why you are interested in this position.  Please apply no later than 16th August 2026. Evaluation of applications will be done on a continuous basis. Please note: The applicant is responsible for ensuring that the application is complete. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview.  For questions, please contact:   Mats Jirstrand, Head of Department, Systems and Data Analysis, Fraunhofer-Chalmers Centre E-mail: mats.jirstrand@fcc.chalmers.se Phone: +46 730 794303