Machine Learning Engineer

OpenMind; San Francisco (CA); On-site; USD 130,000 - 210,000; Full time

Job summary

OpenMind in San Francisco, CA, is building the operating system for robotics. You will be the engineer turning research into models that run reliably on physical robots, handling training, evaluation, and inference systems behind our models.

You will work with continuous real-world embodied data from humanoid and quadruped robots in public spaces. You will ship models as containerized services in our OM1 stack, and own latency, reliability, and field regressions while collaborating with

Qualifications

• MS or PhD in ML, robotics, CS, or related field.
• 1+ years of industry or applied research experience shipping ML models.
• Strong programming in Python/Go/C++ and PyTorch or JAX.
• Experience with multimodal models, perception, or reinforcement learning and imitation learning.
• Edge deployment on hardware with latency/memory constraints (e.g., NVIDIA Jetson).
• Experience with TensorRT, ONNX, or other inference toolchains.
• Experience with ROS2 and Docker-based deployment on robots.
• Experience building datasets or benchmarks used outside your team.
• Publications at CoRL, RSS, ICRA, NeurIPS, ICML, or ICLR.
• Bias toward models that work in the real world, not only on benchmarks.

Responsibilities

• Build multimodal perception models for social awareness (active speaker detection, person tracking, engagement estimation).
• Scale pipelines turning multi-hour logs into labeled datasets for training and evaluation.
• Ship containerized models in the OM1 deployment stack, managing latency and reliability.
• Collaborate with research, robotics engineers, and deployment teams to productionize models on real robots.
• Integrate, fine-tune, and optimize foundation models for real-time perception on embedded compute.

Skills

ML model training

Production ML systems

Multimodal perception

Education

MS or PhD in machine learning, robotics, computer science, or related field

Tools

Python

Go

C++

PyTorch

JAX

TensorRT

ONNX

ROS2

Docker

NVIDIA Jetson

Job description

THE ROLE

OpenMind is building the operating system for robotics: the models that let robots understand people, context, and norms well enough to work alongside humans safely. Our models run on real robots with real customers, and every deployment produces new data to learn from.

You will be the engineer who turns research into models that run reliably on physical robots. You will build the training, evaluation, and inference systems behind our models. You will work with a continuous stream of real-world embodied data, including egocentric video, lidar, audio, state-action logs, and the robot's own reasoning traces, collected from humanoids and quadrupeds operating in public spaces.

WHAT YOU WILL DO

• Build multimodal perception models that make robots socially aware, such as active speaker detection, person tracking across camera and lidar, and engagement estimation

• Build and scale the pipelines that turn multi-hour, full-stack robot logs into curated, labeled datasets for training, evaluation, and external data partnerships

• Ship models as containerized services in our OM1 deployment stack, and own their latency, reliability, and regressions in the field

• Work closely with our research lab, robotics engineers, and deployment teams to move models from prototype to production on physical robots within weeks

• Integrate, fine-tune, and optimize foundation models for real-time perception and reasoning on embedded compute

WHAT WE LOOK FOR

• MS or PhD in machine learning, robotics, computer science, or a related field

• 1+ years of industry or applied research experience training and shipping machine learning models

• Strong engineering fundamentals in languages such as Python, Go, and/or C++, and fluency in PyTorch or JAX

• Hands‑on experience with multimodal models, perception, or reinforcement and imitation learning

• Experience running models on edge hardware under real latency and memory constraints, such as the NVIDIA Jetson software stack

• Experience with TensorRT, ONNX, or other inference optimization toolchains

• Experience with ROS2 and Docker‑based deployment on robots

• Experience building datasets or benchmarks used outside your own team

• Publications at CoRL, RSS, ICRA, NeurIPS, ICML, or ICLR

• A bias toward models that work in the real world, not only on benchmarks