Machine Learning Engineer | Robotics and AI

Embodied Intelligence
Real-World ML / Applied Autonomy

About the Company

We are representing an expanding deep-tech startup focused on building cohesive ecosystems that bridge physical machinery and intelligent software. The company's core mission is to democratize advanced automation by engineering modular, out-of-the-box hardware and software suites that solve tangible challenges in the physical AI space.

Role Responsibilities

• Model Development: Design, optimize, and evaluate sophisticated machine learning models tailored for robotic mobility and dexterity, leveraging both behavior-cloning and reward-based learning methodologies.

• Pipeline Ownership: Manage the end-to-end AI lifecycle, which includes orchestrating diverse, multi-sensor data streams, scaling up model training, and executing final deployments directly onto physical machines.

• Sim-to-Real Iteration: Drive rapid prototyping cycles between virtual environments and the physical world-gathering real-world telemetry, diagnosing edge cases, and continuously driving systemic improvements.

• Infrastructure Scaling: Architect and maintain robust, multi-node GPU training workflows, taking charge of resource allocation, experiment tracking, and model checkpointing.

• Tooling: Build custom internal utilities for deep-dive experiment reviews, visual diagnostics, and telemetry analysis.

• Cross-Disciplinary Collaboration: Work hand-in-hand with mechanical, electrical, and firmware engineering experts to ensure theoretical ML breakthroughs translate into reliable, field-ready automation.

Core Qualifications

• Advanced degree (MSc or Doctorate) in Robotics, Artificial Intelligence, Computer Science, or a closely aligned field, backed by relevant industry tenure.

• Deep programmatic fluency in modern deep learning libraries, particularly PyTorch.

• Demonstrated mastery of physical AI techniques, specifically around policy learning, neural control systems, and agent-based training paradigms.

• A strong portfolio demonstrating the ability to move 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 (such as architectures integrating language, visual inputs, and kinematics), or generative video models.

• Experience handling high-throughput robotic data serialization formats, alongside a strong track record of contributing to premier academic venues (such as ICRA, IROS, NeurIPS, or equivalent).

  To apply online please use the apply function, alternatively you may contact Evangeline. (EA: 94C3609/ R24124002)