# Applied Researcher - Deployment Intelligence at Dyna Robotics

Applied Researcher - Deployment Intelligence at Dyna Robotics in Redwood City, CA. On-site, full-time. Posted July 21, 2026.

- Company: Dyna Robotics
- Location: Redwood City, CA
- Workplace: On-site
- Job type: Full-time
- Posted: 2026-07-21
- Apply: https://jobs.ashbyhq.com/dyna-robotics/09648521-c515-4e1d-a00d-92644b554d0a/application
- Source: https://jobmagic.ai/jobs/applied-researcher-deployment-intelligence-dyna-robotics-ybwt52

## Requirements

- 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.

## 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.*