Scout AI
Senior AI Engineer
$200K–$400K
Sunnyvale, CA +1 · Remote · Senior · Full-time · Posted Jul 8, 2026
- Location
- Sunnyvale, CA
Remote - Experience
- 6+ years of hands-on AI model experience, including 2+ years with multimodal models or agentic AI systems.
- Education
- BS, MS, or PhD in Computer Science, Engineering, Mathematics, Physics, or related technical field, or equivalent practical experience.
- Visa sponsorship
- Must be a U.S. Person due to required access to U.S. export controlled information or facilities; may be expected to obtain and hold a U.S. Top Secret security clearance.
About the job
Develop and deploy multimodal foundation models and AI-agent capabilities that enable teams of robots to plan and execute missions.
What you'll do
- Design, train, and evaluate multimodal foundation models, including model architectures, datasets, and training strategies.
- Build agent memory, communication, planning, tool-use, evaluation, and simulation capabilities for multi-robot systems.
- Integrate AI with onboard autonomy stacks and validate performance through field trials and mission operations.
What they ask for
- 6+ years building and deploying AI models, including 2+ years with multimodal models or agentic AI systems.
- Proficiency in Python, PyTorch, and modern machine learning infrastructure; experience training and evaluating large-scale models on distributed compute.
- Experience improving model performance through data curation, training strategy, evaluation design, and experimentation; grasp of SFT, DPO, RLVR.
- Must be a U.S. Person; must have a relevant technical degree or equivalent practical experience.
Nice to have
- Advanced graduate research in AI, robotics, or machine learning.
- Experience building closed-loop simulation, evaluation, or reinforcement learning environments for agentic or physical AI systems.
Pay
$200K – $400K USD base salary
Tools
Python, PyTorch, SFT, DPO, RLVR
AI focus
Develop multimodal foundation models and agentic AI for autonomous multi-robot mission execution, including training, evaluation, reinforcement learning, planning, memory, coordination, simulation, and edge deployment.
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