Dyna Robotics builds general-purpose robots powered by proprietary embodied AI foundation models that can perform diverse tasks in real-world commercial environments.
Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors. POSITION OVERVIEW As a Software Engineer, Data Infra you are the architect of the "Laboratory" where Dyna’s robotic intelligence is refined. You won't just move data; you will build the interactive systems that bridge the gap between raw multimodal sensor streams and production-ready ML models. This is a high-impact, hands-on role where you will design the infrastructure to visualize model performance, automate data labeling, and manage the "human-in-the-loop" workflows - such as teleoperation and skill capture - that are critical to our scaling. If you enjoy solving complex geometric problems and building the low-friction tools that empower ML researchers, you’ll thrive here. WHAT YOU’LL DO - Interactive Data Systems: Architect the engines and interfaces that unify raw robot logs, video, and 3D sensor data. You will enable seamless "human-in-the-loop" workflows, from episode annotation to analyzing manual interventions. - Signal Extraction & Geometry: Design and implement algorithms to extract structured signals (trajectories, events, 3D poses) from raw captures. Strong mathematical intuition is required to turn pixels and point clouds into ground-truth insights. - Evaluation & Benchmarking: Build high-performance tools to compare model-driven motion against human-captured data, helping the team quantify model progress across diverse tasks. - Scalable ML Pipelines: Build and operate distributed data pipelines (using Python, GCP/AWS, and Kubernetes) for the ingestion, transformation, and validation of terabytes of multimodal data. - Observability & Debugging: Develop visualization tools that make complex model behaviors and sensor data easy to interpret, reducing the time from "data collected" to "model trained." - Startup Fluidity: Collaborate across ML, Robotics, and Product teams. As an early member of the data team, you will help define the roadmap where no blueprint yet exists. WHAT YOU’LL BRING - The Experience: 5+ years of professional software experience, ideally with a focus on data-intensive or "human-in-the-loop" platforms. - Technical Stack: Proficiency in Python (NumPy, Pandas) and a solid understanding of modern backend architectures. Experience with React/TypeScript is a major plus for building internal observability tools. - Mathematical Core: Strong skills in algorithms and geometric calculations (e.g., coordinate transformations, 3D spatial reasoning). - Data Mastery: Hands-on experience with relational and NoSQL databases (PostgreSQL, Redis) and cloud-native infrastructure (GCP/AWS). - Problem-Solving: The ability to debug real-world data issues - from sensor drift to pipeline bottlenecks - independently. BONUS POINTS FOR: - Experience with multimodal data (video, LiDAR, time-series) in robotics or autonomous systems. - Familiarity with Airflow, Kubeflow, or similar distributed batch processing systems. - Experience with experiment tracking frameworks like Weights & Biases or MLFlow. - Experience as an early hire in a fast-paced startup environment. At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect. Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about the intersection of geometry and robotics, we want to hear from you—even if you don't check every box.