Applied Researcher - Deployment Intelligence

Job description

**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. ## The Role Our models don't stop learning at deployment. A growing fleet of robots is generating real production data every day, and the gap between "works in the lab" and "works at a new customer site, forever" is a research problem, not just an ops one. As an **Applied Researcher** on the AI Research team, you'll own that gap: mining fleet sensor and video data for failure modes, building the monitoring that catches problems before customers do, and turning deployment data into continuous, measurable model improvement. This is a hands-on, ship-it role. We care far more about whether you can land a real improvement on the fleet than about producing research for its own sake. ## What You'll Do - **Continuous Learning Loops:** Design and ship pipelines that turn real deployment data (successes, failures, teleop corrections) into targeted fine-tuning and online policy improvement, closing the loop from field to model without a full retrain cycle every time. - **Fleet Data Analytics:** Mine high-frequency multimodal sensor and video data across tens of thousands of fleet episodes to catch failure modes, drift, and regressions before they become customer-visible. - **RL for Deployment:** Apply reinforcement learning (offline RL, RL fine-tuning, reward modeling from human and teleop feedback) to improve policies directly from real-world deployment data, not just simulation. - **Automated Fleet Monitoring:** Build automated monitoring that flags anomalies, near-failures, and out-of-distribution scenes across the fleet in real time, and that decides what needs a human versus what the system can self-correct. - **Cross-Scene Generalization:** Characterize and close generalization gaps as robots move to new sites, lighting, layouts, and objects; build the evaluation harnesses and data-selection strategies that make day-one performance at a new customer site predictable. - **End-to-End Ownership:** Partner with Research, Data, and Deployment teams to turn a finding into a shipped improvement, from a data-analysis notebook to a production monitoring dashboard to a deployed model update. ## What You'll Bring - **Bias to Ship:** You're happiest closing the loop, landing a fix on the fleet and watching it hold up at a real customer site, rather than polishing a benchmark number or a paper. We want someone driven by shipped impact and genuine passion for the problem, not research for its own sake. - **Educational Background:** Bachelor's, Master's, or PhD in CS, Robotics, Statistics, or a related field, or equivalent practical experience. Degree level doesn't matter to us; what matters is genuine passion for the work and a track record of hands-on effort that shipped into a real system, not just a benchmark. - **Applied ML Depth:** Hands-on experience in at least two of: reinforcement learning, sensor-data modeling/anomaly detection, vision-language models, or continual/online learning. - **Production Instincts:** Experience building monitoring, evaluation, or data pipelines for a live ML system, comfortable with the ambiguity of real-world fleet data versus curated benchmarks. - **Experimentation & Statistics:** Comfortable designing and reading production experiments (A/B tests, canary rollouts, staged fleet deployments) and applying enough statistical rigor to tell a real regression from noise in messy real-world data. - **Technical Stack:** Strong Python and PyTorch (or JAX); comfortable with large multimodal datasets and distributed compute (Slurm/GPU clusters). - **Communication:** Able to turn a fleet-scale data investigation into a clear recommendation that researchers and operators can act on. ## Bonus Points For - Experience with robot fleets or other physically-deployed autonomous systems in the field, not just simulation. - Experience building or fine-tuning perception or foundation models for automated monitoring, captioning, or anomaly detection. - Background in statistical methods for detecting anomalies and drift (change-point detection, forecasting) applied to sensor or telemetry data. - **Experience with human-in-the-loop learning:** reward modeling from operator corrections, active learning, or data curation from failure cases. *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 robotics, no matter your discipline, we want to hear from you, even if you don't check every box.*