Senior Research Scientist (Robotics AI)

Location: Boston, MA; on-site

Company

hirify.global develops risk-aware, reliable, field-ready AI systems for real-world robotics and embodied intelligence.

What you will do

• Advance research in general-purpose humanoid manipulation and loco-manipulation.
• Develop model architectures, learning objectives, action representations, and training methods for large-scale robot learning.
• Research robotics foundation models, including vision-language-action and other multimodal behavior models.
• Design experiments, benchmarks, evaluation protocols, and ablation studies to measure robustness, precision, dexterity, generalization, and long-horizon performance.
• Lead projects from initial hypotheses through large-scale training, deployment, and validation on physical humanoid robots.
• Partner with research engineers and systems teams to turn algorithms into reproducible training pipelines and reliable robot capabilities, while mentoring researchers and engineers.

Requirements

• PhD in Robotics, Computer Science, Machine Learning, Electrical Engineering, Mechanical Engineering, or a closely related field.
• Strong research record in robot learning, robotic manipulation, reinforcement learning, imitation learning, multimodal learning, or a related area.
• Demonstrated ability to lead research projects through rigorous experimental validation.
• Deep expertise in foundation models, vision-language-action models, generative policies, reinforcement learning, imitation learning, or multimodal foundation models.
• Experience training and evaluating modern deep-learning models using PyTorch.
• Strong understanding of robotic manipulation, including kinematics, dynamics, control, perception, and planning, plus experience deploying learning-based methods on physical robotic systems.

Nice to have

• Research experience with humanoid robots, bimanual systems, or multi-fingered robotic hands.
• Experience with multimodal transformers, diffusion or flow-based policies, reinforcement-learning fine-tuning, offline RL, reward modeling, or preference learning.
• Research on cross-embodiment transfer, multi-robot learning, embodiment-general action representations, or large-scale robotics datasets.
• Experience combining autonomous rollouts, simulation, web-scale data, teleoperated data, and other heterogeneous data sources.

Culture & Benefits

• Work on real-world robotic systems and contribute to hirify.global's humanoid manipulation and autonomy roadmap.
• Collaborate across research, engineering, systems, and product teams.
• Communicate research through technical reviews, publications, presentations, and open-source releases where appropriate.
• Balance fundamental research with near-term advances in reliable field deployment.