Robotics | Embodied AI | Research Scientist
Summary
Research scientist builds and deploys AI models that control real-world robots, iterating between simulation and physical hardware using PyTorch and deep learning.
Scale physical intelligence beyond just the simulation!
about the company
We are representing a well-funded, hyper-growth deep-tech startup developing next-generation Embodied AI technology. The company's mission is to revolutionize industrial automation by creating generalizable, intelligent software platforms capable of controlling complex physical systems in the real world. If you want to see your code move physical machinery in real-time, this is where you belong!
about the role
As a Senior ML & Autonomy Engineer, your primary focus will be bridging the gap between theoretical AI models and real-world physical motion You will move fast, iterate daily, and drive rapid prototyping cycles between virtual environments and real-world hardware.
- Model Development & Deployment: Design and optimize sophisticated ML models tailored for robotic mobility and dexterity (leveraging behavior-cloning and reward-based learning), taking them from conception directly onto physical machines.
- Pipeline & Infrastructure Scaling: Manage the end-to-end AI lifecycle by orchestrating multi-sensor data streams, maintaining robust multi-node GPU training workflows, and tracking large-scale experiments.
- Sim-to-Real Iteration: Gather real-world telemetry, diagnose edge cases, and continuously bridge the gap between virtual environments and the physical world.
- Tooling & Collaboration: Build custom internal utilities for deep-dive visual diagnostics and telemetry analysis while partnering closely with hardware and systems engineering teams to ship field-ready automation.
skills and experience
- Education & Tenure: Advanced degree (MSc or PhD) in Robotics, AI, Computer Science, or a closely aligned field, backed by solid industry experience.
- Technical Stack: Deep programmatic fluency in modern deep learning libraries, specifically PyTorch.
- Physical AI Mastery: Proven expertise in policy learning, neural control systems, and agent-based training paradigms.
- Sim-to-Real Execution: A strong portfolio demonstrating your ability to pull algorithms out of purely simulated academic benchmarks and successfully deploy them onto functional, physical hardware.
Preferred Bonus Skills:
- Practical exposure to large-scale, multimodal foundation models guiding physical agents (architectures integrating language, visual inputs, and kinematics) or generative video models.
- A track record of contributions to premier academic venues (ICRA, IROS, NeurIPS, CVPR, or equivalent).
To apply online please use the 'apply' function, alternatively you may contact Evangeline. (EA: 94C3609/ R24124002)