Build the data infrastructure that powers physical AI.

Physical AI is moving from research labs into production fleets across industries. As robots scale across the real world, from factories to vehicles, to defense - every workflow from product development to deployment becomes a data problem: what happened, when, on which robot, and why?

At Foxglove, we built the unified data platform for physical AI that developer and engineering teams use to answer those questions. We help teams make vast quantities of robotics 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.

What you’ll do

• 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/Databricks or large-scale data processing

• Exposure to infrastructure-as-code (Terraform), Kubernetes, or cloud provider administration

• Interesting personal projects that solved a real problem

Why join Foxglove

• Work on real robotics problems. Robot data is large, messy, multimodal, time-sensitive, and tied to physical-world behavior. The problems we work on span ingestion, indexing, search, visualization, replay, connectivity, collaboration, evaluation, and operations.

• Build tools engineers rely on. Foxglove is used by robotics teams investigating failures, validating changes, reviewing field behavior, curating datasets, and operating production fleets. The work you do helps teams understand what their robots saw, what they did, and why they behaved the way they did.

• High-leverage product surface area. A better query path, visualization workflow, Fleet connection, UI primitive, API, onboarding flow, or customer deployment can change how an entire robotics team works.

• Ownership and autonomy. We’re a small team, and people at Foxglove own meaningful work end-to-end. You’ll have real influence over product direction, technical architecture, customer outcomes, and how we operate as a company.

• Strong peers and high standards. You’ll work with people who care about correctness, performance, craft, product judgment, and building software that technical users trust under pressure.

• A mission grounded in production software. We accelerate robotics and physical AI by building the infrastructure teams use every day to connect to robots, inspect live telemetry, manage multimodal data, replay runs, investigate failures, and improve real systems.

Learn more about how we hire and work foxglove.dev/careers

Equal opportunity

Foxglove is an equal opportunity employer. We welcome candidates from different backgrounds, experiences, and communities, and we’re committed to building an inclusive environment for everyone.

We encourage you to apply even if you don’t meet every nice-to-have listed above. The strongest candidates often bring a mix of relevant experience, curiosity, judgment, and the ability to learn quickly.

About Foxglove

Foxglove is the data platform for Physical AI. Built for robotics teams developing real-world systems, Foxglove provides a purpose-built, modular platform to collect, organize, and learn from vast quantities of multimodal data, creating the data flywheel to safely scale from development to distributed fleets. Founded in 2021, Foxglove supports hundreds of customers across automotive, aerospace, defense, logistics, agriculture, construction, and consumer robotics to deploy the next generation of intelligent machines. Learn more at foxglove.dev .