Jobs at Dyna Robotics

Own continuous learning for deployed robot fleets: build pipelines to convert deployment data into fine-tuning and policy updates, mine multimodal fleet data for failures and drift, apply RL and reward modeling, create automated real-time monitoring and evaluation, and partner across teams to ship measurable improvements to production robots.

The role involves applied research and engineering for deploying AI in robotics, focusing on infrastructure design, fleet management, and software stability.

Lead high-volume, full-cycle recruiting for operations roles (operators, teleoperators, technicians). Source at scale, coordinate screens/interviews/onboarding, partner with operations leaders to forecast headcount, track and improve hiring metrics, and streamline ATS workflows and automation to maintain fast, high-quality hiring while delivering a positive candidate experience.

Own hardware product strategy for next-generation robots: define requirements for mobility, sensing, and end-effectors; translate field learnings into specs; drive delivery through DVT to production; prioritize reliability, serviceability, and tradeoffs across cost, capability, and timeline while aligning cross-functional teams.

Lead QA function across Dyna's robotics stack, managing a team, developing automated test frameworks, and ensuring hardware behavior validation.

The Marketing and Communications Lead will shape Dyna's narrative, manage PR, lead content strategy, and attract talent while enhancing brand visibility in the robotics and AI sectors.

The Research Engineer/Scientist will develop AI models for robotic manipulation, design learning algorithms, analyze results, and collaborate with cross-functional teams to enhance performance.

The Deployment Success Manager will oversee customer deployments, manage operational execution on-site, and ensure stakeholder communication, tracking project timelines and addressing any operational challenges that arise during deployment.

The AI Data Strategist role focuses on defining data requirements to drive model improvement across robotic platforms, emphasizing data collection priorities, evaluation frameworks, and lifecycle observability.

Develop and lead software for robotic systems, focusing on control, sensing, teleoperation, and integration with AI and hardware teams.

Lead and build a blended engineering organization across hardware, software, AI systems, and deployment. Hire and retain teams, set operating cadence, remove bottlenecks, drive cross-functional initiatives from research to field validation, and align vision to execution for reliable customer deployments.

This role involves leading business operations, addressing operational bottlenecks, supporting customer deployments, and managing various analytical tasks in a high-growth startup.

Deploy and maintain on-site robotics systems: network bring-up, inference server provisioning, incident response, cross-stack debugging (Linux, containers, GPUs, hardware), customer coordination, runbook and automation creation, and reporting issues back to engineering.

Manage interview scheduling and logistics, maintain ATS accuracy, coordinate candidate travel and onboarding, follow up for timely feedback, and support HR ops to ensure a smooth hiring experience.

Design and build data systems for robotic intelligent workflows, focusing on automation, signal extraction from multimodal data, and ML model evaluation.

Accepting applications for a limited number of research-focused internships in robotics. Seeking candidates passionate about the intersection of geometry and robotics; values problem-solving, grit, technical rigor, and diversity. Open to applicants at all levels.

Annotate data on robot tasks to improve AI performance. Maintain data integrity, apply guidelines, and use pre-annotation tools for quality checks.

Short-term (60-day) temporary role to collect high-quality data by following SOPs. Set up workstations and equipment, perform repetitive collection tasks, report issues, maintain cleanliness, meet productivity and quality targets, and accept coaching to improve performance.

Lead hardware builds, diagnostics, validation, and quality control for robotic systems. Assemble and troubleshoot electro-mechanical assemblies, develop test plans and fixtures, drive root cause analysis, maintain SOPs and metrics, support deployments, and mentor junior technicians.

The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.