Field AI

Industry:
Robotics
Location:
Job Type:
Artificial Intelligence
Senior Engineering Manager, Robotics and Humanoid Research
Job Description:
FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.
We are seeking an accomplished Senior Engineering Manager to lead a lean, high-performing team of research scientists and engineers working at the forefront of robotics AI. In this role, you will define and drive research strategy, oversee the development of advanced AI algorithms, and guide programs from early research through real-world deployment on physical robotic systems. You will balance deep technical leadership with people management, ensuring both scientific excellence and real-world impact.
 
This is an opportunity to shape the future of robotic autonomy by translating cutting-edge research into systems that operate reliably in complex, dynamic environments on real robots.
Job Responsibilities:
  • Set Research Vision and Strategy
  • Define and execute the robotics AI research roadmap in alignment with FieldAI’s mission and product goals.
  • Identify emerging research directions, tools, and techniques across robotics, embodied AI, and machine learning.
  • Establish clear technical goals, milestones, and success metrics tied to real-world robot performance.
  • Lead and Grow Research Teams
  • Recruit, mentor, and manage a multidisciplinary team of research scientists and engineers.
  • Provide hands-on technical leadership through design reviews, experiment reviews, and close collaboration on challenging problems.
  • Own performance management, professional development, and career progression within the team.
  • Drive End-to-End Research and Deployment
  • Lead projects from problem definition through algorithm design, simulation, on-robot testing, and field deployment.
  • Ensure research outputs translate into robust, scalable, and safe systems on physical robots.
  • Champion experimental rigor, including benchmarking, ablations, reproducibility, and failure analysis.
  • Partner Across the Organization
  • Collaborate closely with product, engineering, and operations teams to integrate research into production systems.
  • Communicate technical progress, risks, and tradeoffs clearly to cross-functional stakeholders and leadership.
  • Represent Field AI externally through publications, conferences, collaborations, and recruiting.
  • Ensure Operational Excellence
  • Build and maintain processes for experiment management, evaluation, and reproducibility.
  • Manage resources, timelines, and priorities to deliver high-quality results efficiently.
  • Foster a culture of technical excellence, ownership, and rapid iteration.
  • Job Requirements:
  • PhD in Computer Science, Robotics, Artificial Intelligence, or a related field, or equivalent practical experience, with a strong background in applied research.
  • 8+ years of experience delivering impactful research in robotics AI, machine learning, or closely related domains.
  • 4+ years of leadership experience managing research and engineering teams, including hiring, mentorship, performance management, and career development.
  • Proven ability to lead multiple complex projects in parallel, from early research through real-world validation.
  • Direct, hands-on experience developing, testing, and deploying AI models on humanoid and/or quadruped robots.
  • Bonus Qualifications:
  • Experience with large-scale field robotics deployments.
  • Deep expertise in one or more areas such as robotics reinforcement learning, manipulation, perception, planning, or multi-robot systems.
  • Demonstrated success with sim-to-real transfer on legged or humanoid platforms.
  • Experience deploying AI on embedded or edge systems in resource-constrained environments.
  • Publications or impactful open-source contributions in robotics or embodied AI.
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