Machine Learning Engineer, Intern
We're building the data engine behind Physical AI — the perception and spatial-reasoning systems that let robots understand the real world. As our Machine Learning Engineer Intern, you'll work alongside the team on the pipelines and models that turn raw multi-sensor data into training-grade datasets, and help fine-tune the computer vision models that depend on them.
We're a high-growth company: you'll work directly with the founders and with many world-tier robotics companies, in a hands-on role that spans training detectors, writing data-cleaning algorithms, and reasoning about geometry in a SLAM stack.
What you'll do
- Fine-tune and evaluate computer vision models (YOLO and similar detection/segmentation architectures) for real-world Physical AI tasks, and iterate on them based on data and failure analysis.
- Help build data-cleaning algorithms and pipelines — deduplication, outlier and mislabel detection, automated QA checks, and label-consistency tooling — to produce high-quality datasets at scale.
- Contribute to SLAM-related applications and tooling: ingest and validate camera/IMU data, support mapping and localization workflows, and surface data issues that degrade spatial accuracy.
- Help define and track data-quality metrics, and build dashboards and validation gates that catch quality regressions across the pipeline.
- Work closely with perception, robotics, and ML teammates to translate model failures into concrete data improvements.
What we're looking for
- Currently pursuing (or recently completed) a BS/MS/PhD in CS, EE, robotics, or a related field, available for a full-time in-person internship in San Francisco.
- Hands-on experience training or fine-tuning CV models (YOLO, Faster R-CNN, SAM, or similar) through coursework, research, or projects, and solid fundamentals in CNNs, object detection, and segmentation.
- Strong Python (PyTorch and the scientific stack), and comfort writing code for data processing, validation, or quality assurance.
- Familiarity with SLAM concepts (e.g., ORB-SLAM, RTAB-Map), multi-sensor data (cameras, IMUs, LiDAR), or sensor fusion and calibration.
- Strong attention to detail, a quantitative mindset about data quality, and the ability to collaborate across perception and robotics teams.
Nice to have
- Experience with 3D computer vision or probabilistic state estimation (Kalman filtering, pose estimation).
- Model optimization for deployment (ONNX, TensorRT, quantization/pruning) or GPU/cloud training infrastructure.
- Exposure to active learning, auto-labeling, or human-in-the-loop annotation systems.
- Coursework or project experience in robotics, autonomous vehicles, AR/VR, or other Physical AI domains.
Compensation
- Competitive hourly rate + equity consideration for exceptional interns. Return-offer track for full-time.
Why join
You'll work in person in San Francisco alongside a team building foundational infrastructure for embodied intelligence, with real scope over the models and data quality that determine how well our systems perceive the physical world, plus the rare intern experience of shipping something that goes straight into production and into customers' training runs.