Robotic AI Engineer/Applied Scientist - Foundation Models

San Francisco Bay AreaData Scientist, AI Engineer$135k–$230kPosted Apr 20, 2026
Back to jobsRobotic AI Engineer/Applied Scientist - Foundation ModelsSan Francisco Bay Area, California USAApplyCompany Overview Maven Robotics is building the world’s leading general-purpose robots and providing physical AI solutions for the most challenging industrial autonomy tasks. Operating in stealth, we are assembling a team of world-class innovators who think from first principles. Our mission is to achieve human-level task success rates in complex environments, even when faced with limited fine-tuning data or evolving robotic hardware. We value unwavering truth-seeking, humility, and relentless determination. Role Description We are seeking exceptional AI researchers and engineers to architect the neural backbone of our general-purpose robots. You will design the Vision-Language-Action (VLA) frameworks or Action World Models that allow our robots to reason, adapt, and succeed where traditional automation fails. Note on Leveling: We are hiring across all levels (from early-career to staff/principal). You are not expected to be a master of every domain listed below; however, you must be able to justify world-class excellence in at least one core factor (e.g., model architecture, RL formulations, or high-scale data systems). In this role, you will: Architect Embodied Foundations: Design model architectures (VLA, World Models, etc.) that achieve ultra-high task success rates with minimal human demonstrations. Master Data Efficiency: Develop novel co-training strategies and efficient learning algorithms that leverage diverse data sources—from Internet-scale video to sparse, high-fidelity human interventions. Generalize Cross Embodiments: Build models capable of zero-shot or few-shot adaptation to new robot configurations, maintaining a high success rate even when proprietary hardware and actuation systems evolve. Innovate Real-World RL: Formulate and deploy novel Reinforcement Learning and policy extraction methods specifically designed for physical, real-world manipulation. Design the Data Loop: Collaborate on advanced data collection systems to capture critical human intervention data for model bootstrapping. Qualifications Must-have: MS or PhD in CS, Robotics, Machine Learning, or a related field (or equivalent industry experience). Deep Technical Mastery: Advanced understanding of transformers, multi-modal alignment, and mapping perception to high-frequency motor control. Specialized Excellence: Proven ability to innovate—not just implement—within one or more areas: VLA models, Real-world RL, or large-scale Data Infrastructure. Software Excellence: Expert-level Python and deep familiarity with PyTorch or JAX Nice-to-have: A track record of high-impact publications (NeurIPS, ICRA, RSS, CVPR) or significant open-source contributions. Experience with large-scale distributed training and model compression. Experience with deployment of models to edge devices (NVIDIA Jetson/Orin) for real-time inference. General knowledge of robotics principles (kinematics, dynamics). Why Maven Robotics? You will be working on a unique in-house robot platform that is evolving alongside your models. We don't just want to reach 90% success; we are building the architectural breakthroughs required to reach 99.9% in the real world.Create a Job AlertInterested in building your career at Maven Robotics? Get future opportunities sent straight to your email.Create alertApply for this job*indicates a required fieldQuick Apply with MyGreenhouseFirst Name*Last Name*Email*PhoneCountryPhoneResume/CV*AttachAttachDropboxGoogle DriveEnter manuallyEnter manuallyAccepted file types: pdf, doc, docx, txt, rtfCover LetterAttachAttachDropboxGoogle DriveEnter manuallyEnter manuallyAccepted file types: pdf, doc, docx, txt, rtfLinkedIn ProfileHow did you hear about us?*Select...What type of employment are you seeking?*Select...Will you now or in the future require visa sponsorship?*Select...Voluntary Self-Identification For government reporting...

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