Staff Engineer, Reinforcement Learning (R3639) - Shield AI

Job Title: Staff Engineer, Reinforcement Learning (R3639)
Job Location: San Diego, California
Job Listing URL: https://jobs.lever.co/shieldai/e5c95f48-8178-4322-87f7-067e7f75258d
Job Description: Founded in 2015, Shield AI is a venture-backed deep-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include the V-BAT and X-BAT aircraft, Hivemind Enterprise, and the Hivemind Vision product lines. With nine offices and facilities across the U.S., Europe, the Middle East, and the Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

JOB DESCRIPTION:

Founded in 2015, Shield AI is a venture-backed defense technology company with the mission of protecting service members and civilians with intelligent, autonomous systems. Its products include Hivemind Enterprise—EdgeOS, Pilot, Commander, and Forge—as well as V-BAT and Sentient Vision Systems (wide-area motion imaging software). With offices in San Diego, Dallas, Washington, D.C., Abu Dhabi (UAE), Kyiv (Ukraine), and Melbourne (Australia), Shield AI’s technology actively supports U.S. and allied operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X and Instagram.

The Hivemind Pilot team is an agile group of engineers focused on building a state-of-the-art Autonomy Software Development Kit (SDK) that enables resilient autonomy and intelligence for a wide range of platforms in diverse environments. The Behavior and Motion Planning team in Pilot develops and integrates algorithms that enable robots to make smart decisions and navigate safely. The team also rigorously tests these systems to ensure reliable performance in real-world environments.

As a member of Behavior and Motion planning team, you will leverage your expertise in robotics and Reinforcement Learning (RL) to develop, deploy, and optimize models for autonomous systems that operate in complex, real-world environments. You will collaborate with cross-functional teams to deliver robust, scalable solutions that advance the state of Hivemind SDK. You will also contribute to developing technical requirements, test plans, and validating the performance of algorithms and models.
What You'll Do:
Design, implement, and deploy reinforcement learning algorithms for a variety of platforms
Collaborate with teams across the organization to integrate RL solutions that meet customer specifications
Analyze and optimize performance of deployed RL models in dynamic environments
Develop tools and infrastructure to support large-scale training, simulation, and evaluation
Mentor and provide technical guidance to junior engineers
Stay current with the latest advancements in RL, and apply them to solve challenging problems
Contribute to the design and architecture of scalable, maintainable software systems
Required Qualifications:
Master's degree in Computer Science, Robotics, or a related field and 5+ years of relevant professional experience or PhD with 4+ years of relevant experience.
Familiarity with prototyping in Python is welcome, but this role demands professional C++ production deployment skills. Candidates whose primary experience is in Python are unlikely to find this position a good fit.
Demonstrated experience deploying reinforcement learning algorithms in production environments
Strong background deploying RL algorithms in production following full Software development lifecycle
Ability to independently deploy high-reliability code suitable for real-world autonomous systems
Experience with RL frameworks (e.g., TensorFlow (C++), libtorch, etc.) and RL training environments (e.g., OpenAI Gymnasium, Google DeepMind Control Suite, etc.)
Solid understanding of software engineering best practices, including version control, testing, and CI/CD
Familiarity with CUDA
Excellent problem-solving and communication skills
Preferred Qualifications:
Peer reviewed publications related to RL
Strong background in Robotics or autonomous systems
Experience with multi-agent RL or distributed RL systems
Familiarity with simulation environments (e.g. Isaac Sim, MuJoCo)
Experience with cloud-based training and deployment
Experience working in aviation, or other safety-critical domains