# Applied Researcher - Deployment Intelligence at Dyna Robotics > Onsite · United States · Unspecified · Other · open. Source: https://alderhq.app/jobs/ashby%3Adyna-robotics%3A09648521-c515-4e1d-a00d-92644b554d0a (read 2026-11-06). ## Facts (observed) - Company: [Dyna Robotics](https://alderhq.app/companies/dyna-robotics) - Location text: Redwood City, CA - Where it can be done from (inferred): Onsite · United States (evidence: "Location: Redwood City, CA") - Posted: 2026-07-21 (108 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/dyna-robotics/09648521-c515-4e1d-a00d-92644b554d0a - Skills listed (inferred): Python, PyTorch ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 2393): p25 $100k, median $156k, p75 $210k ## Description (the employer's text) 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. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Adyna-robotics%3A09648521-c515-4e1d-a00d-92644b554d0a and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.