ML Systems Engineer, Robotics & Autonomy

Our work We are a well capitalized stealth VC-backed startup building a new type of spatial AI capable of universally solving autonomy. We innovate at the foundational layer of AI by training our own AI models. Our team Our team is composed of AI pioneers and leaders from Google X, Google Brain, and Unity. Several of us are repeat founders, with deep commercialization insights across multiple enterprise segments. We enjoy long, deep ideations around entirely unexplored AI use cases in autonomy (cars, drones, robots). Who you are Curious : you have an innate curiosity for all things intellectual, and it’s something you can’t turn off. You obsess over finer details others miss. High intensity : you thrive in a high-stakes environment, and are driven by an innate obsession, not by others. Fast learner : you gravitate toward learning new things, and often find yourself learning more quickly than everyone around you. Driven by discomfort : you enjoy leaving your comfort zone and challenging yourself Creative : track record of solving hard problems with solutions worthy of academic papers Educator : you take pride in your ability to communicate complex topics clearly and have excellent speaking and writing skills. Zero ego : you don’t just take feedback, but truly see it as a gift. You don’t wait until feedback is given, but solicit it with every opportunity. Focused on results : you roll up your sleeves to help solve the most pressing problem on a given day, even if it has nothing to do with this job post. Where you are West or East Coast USA, San Diego Preferred Technical skills Broad technical foundation in software and systems engineering, with hands-on experience building and deploying production-grade systems for robotics or autonomous platforms. Systems engineering: experience designing, building, and maintaining complex software systems in robotics, autonomous vehicles, or drone platforms Flight control & autonomy: experience working with flight control software such as PX4 or ArduPilot is a strong plus Eval pipelines: experience designing and running evaluation pipelines for robotics or autonomous systems — including simulation, hardware-in-the-loop, and real-world benchmarking Data pipelines: experience writing preprocessing and ingestion pipelines for large-scale model training and fine-tuning (image foundation model) Model optimization: experience optimizing models for production deployment — quantization, pruning, distillation, TensorRT, ONNX, or edge inference frameworks Programming: deep proficiency in Python; C++ experience is a strong plus for real-time systems work ML foundations: solid understanding of deep learning theory and practice, especially computer vision and spatial reasoning (PyTorch, OpenCV) Backend: experience designing and building scalable, high-availability server-side systems (Python, NodeJS, or Golang) Cloud & infrastructure: familiarity with DevOps and infrastructure tooling — Docker, Kubernetes, GPU scheduling — on GCP or AWS Monitoring: experience with logging and observability tools such as DataDog, Sentry, and/or CloudWatch Version control: deep familiarity with Git and collaborative development workflows on GitHub or GitLab Domain-specific: familiarity with spatial data, geographic information systems, or spatial databases (PostGIS) is a plus #J-18808-Ljbffr