Software Engineer, New Grad

Build the data infrastructure that powers robots in the real world. Robotics is moving from research labs into production fleets across factories, warehouses, vehicles, defense systems, agriculture, logistics, and field deployments. As robots scale across the physical world, every failure, regression, edge case, and unexpected behavior becomes a data problem: what happened, when, on which robot, and why? Every robot, in every industry, requires the same core capabilities: to sense, understand, and act on multimodal data from the physical world. At Foxglove, we built the agentic data platform robotics and Physical AI teams use to answer those questions. We help robotics teams make vast quantities of robot data actionable, creating the data flywheel they need to develop, test, train, deploy, and operate robots with confidence. About the Role We're looking for a new grad Machine Learning Engineer to join our team building the ML infrastructure that powers robotics and autonomous systems at scale. You'll work at the intersection of applied ML and production systems — from selecting and deploying models against high-cardinality multimodal robotics data to building the foundational ML tooling that robotics engineers rely on every day. This role is ideal for someone who's excited about physical AI and wants to ship things that work in production, not write papers. Key Responsibilities - Building and owning inference infrastructure — model serving, scaling, latency/cost optimization (think TorchServe, vLLM, Triton) - Selecting models for object detection/understanding, embedding computation, text captioning, and more — applied against high-cardinality, multimodal robotics data (video, point clouds, timeseries) - Standing up semantic search over petabyte-scale robotics data using vector databases and embedding models - Designing evaluation and training pipelines so the team can iterate quickly on model performance - Ingesting and serving massive volumes of sensor data through batch and realtime pipelines - Developing product features to help robotics engineers organize, search over, and serve their data for training ML models - Making real build-vs-buy decisions on cloud architecture across multi-cloud environments (GCP, AWS, Azure) - Collaborating with product engineers to ship features that go directly to customers building robotics and autonomous systems Our Technical Stack - Rust, TypeScript, PostgreSQL - Kubernetes - GCP, Azure, and AWS - Agentic coding tools (Claude Code / Cursor) What We're Looking For - Bachelor's or Master's degree in Computer Science, Robotics, or a related field (recent graduates or graduating in 2026) - Hands-on ML experience — through internships, research, or academic projects — with a bias toward applied/production work over research - Familiarity with ML frameworks and inference tooling (e.g., PyTorch, TorchServe, vLLM, Triton, or similar) - Experience writing software in Python, Rust, C++, or TypeScript - Exposure to distributed systems concepts, cloud infrastructure (GCP, AWS, Azure), or data-intensive applications - Familiarity with vector databases, embedding models, or retrieval systems - Familiarity with SQL databases and an interest in query engines, big data storage and retrieval, and data-intensive systems - Passion for building technical tools where engineers are the primary users - Excellent written and verbal communication skills - Eagerness to learn and thrive in a fast-paced, small team environment - A mindset that considers customer impact when making technical decisions Bonus Points - Experience with model optimization techniques — quantization, distillation, mixed precision, or TensorRT - Familiarity with fine-tuning or domain adaptation for vision, language, or multimodal models - Experience with sensor data pipelines (lidar, camera, IMU, etc.) or autonomous vehicle software stacks - Published robotics or ML research, or contributions to open-source projects - Experience with Spark/Dat