ML Infrastructure Engineer

Job Description

We are seeking an ML Infrastructure Engineer to build and scale the machine learning systems that power our intelligent robots. In this role, you will design and maintain the infrastructure for training, evaluating, and deploying the ML models that enable robot locomotion, perception, manipulation, navigation, and human-robot interaction. Your work will directly impact how quickly our research translates into real-world robotic capabilities.

You'll work at the intersection of machine learning and systems engineering, ensuring our ML training and deployment systems are robust, efficient, and scalable as we grow from prototype to production.

Key Responsibilities

• Design and build scalable ML training infrastructure, including distributed training pipelines and GPU cluster management both in the cloud and on-prem.

• Develop systems for experiment tracking, model versioning, and reproducibility.

• Build deployment infrastructure for serving ML models on robotic hardware with strict latency requirements.

• Optimize model inference for edge devices and embedded systems.

• Collaborate with research teams to accelerate the path from experimentation to production.

• Contribute to data pipelines and labeling infrastructure as needed, in partnership with the data platform team.

Required Skills & Qualifications

• Education : Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience.

• Work Experience : 3+ years of experience in ML infrastructure, MLOps, or related roles.

• Technical Expertise :

  • Strong programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, JAX).

  • Experience with ML infrastructure tools (e.g., MLflow, Weights & Biases, Kubeflow, Ray).

  • Proficiency with containerization (Docker) and orchestration (Kubernetes).

  • Understanding of distributed systems and GPU computing.

Nice-to-have Skills

• Experience with robotics data (sensor streams, video, point clouds) and real-time inference systems.

• Familiarity with model optimization techniques (quantization, pruning, distillation).

• Experience with reinforcement learning or simulation-based training pipelines.

• Experience with cloud platforms (AWS, GCP) and hybrid on-prem/cloud architectures.

What We Offer

• The opportunity to work on groundbreaking robotics technology, enabling the next generation of humanoid robots to interact dynamically with their environments.

• A collaborative and innovative environment that fosters creativity and exploration.

• Equity ownership in the company

• Health Benefits (Medical, Dental, and Vision)

Compensation

$140k - $220k/yr, plus equity