Design and implement post-training pipelines for VLA models using techniques such as reinforcement learning (RL), reinforcement learning from human or preference feedback (RLHF/RLAIF), in-context learning.
Develop methods to enhance real-world transferability of policies trained in simulation.
Explore and implement reset-free and autonomous data collection strategies that enable continual skill improvement without manual resets or supervision.
Investigate exploration algorithms that balance safety, curiosity, and efficiency for data gathering in both simulation and real-world robotic systems.
Lead the design of data collection and curation pipelines for exploration and post-training.

Requirements

Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or related field, or equivalent applied research experience.
Expertise in reinforcement learning, imitation learning, and multimodal representation learning.
Strong proficiency with deep learning frameworks (e.g., PyTorch, JAX) and robotics simulation environments (e.g., MuJoCo, IsaacSim, PyBullet, Habitat).
Experience with sim-to-real transfer, policy adaptation, or continual learning in embodied settings.
Strong coding and experimental skills with an emphasis on reproducibility and evaluation at scale.
Prior robotics experience with real-world hardware and ML-based robot deployments.

Benefits

medical, dental, and vision insurance
401(k) eligibility
paid time off benefits (including vacation, sick time, and parental leave)
annual cash bonus structure

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

reinforcement learningimitation learningmultimodal representation learningdeep learningsim-to-real transferpolicy adaptationcontinual learningdata collectiondata curationexperimental skills

Soft Skills

leadershipproblem-solvingcuriosityefficiency

Certifications

Ph.D.M.S.