Dyna Robotics · San Francisco
OtherEngineeringFullTimeposted 2mo ago
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.
We're looking for a senior engineering leader to build and lead Dyna's blended engineering organization across hardware, software, AI systems, and deployment-facing product work. This leader will create the team structure, operating rhythm, communication loops, and execution discipline that turn research breakthroughs into reliable customer deployments.
This is not a pure technical architect role or a staff-plus IC role. You should understand complex hardware-software and AI-enabled systems deeply enough to ask the right questions, align experts, and connect research, software, hardware, and field deployment decisions. Your primary mandate is organizational: build the team, retain strong people, manage progress, share context, and ensure execution across ambiguous, high-stakes work.
This is an on-site role based in Redwood City. You will work closely with Dyna's founders, senior leadership team, ML Research, Hardware, Software, Operations, Product, and Deployment. The right candidate understands that Dyna's advantage comes from tightly coupling research, engineering, and real-world deployment feedback, not from treating research and commercialization as separate handoffs.
Team Building & Retention: Hire, onboard, grow, and retain ICs and managers; build leadership capacity, clear ownership, useful feedback loops, and career paths before the organization outgrows them.
Operating Cadence & Resource Bottlenecks: Set planning cadences, milestones, owners, decision forums, and progress visibility while identifying bottlenecks across people, decisions, dependencies, hardware access, deployment support, and cross-team capacity.
Execution Ownership: Drive large, cross-functional initiatives from definition through field validation, launch, and post-deployment learning; define readiness criteria and operating metrics so quality, reliability, and safety risks are visible before deployment.
Research-to-Deployment Loop: Partner with research, software, hardware, and operations so customer deployment feedback becomes actionable engineering and research priorities.
Vision & Context Alignment: Translate leadership-team vision into clear execution context for managers and ICs, keeping teams aligned on goals, tradeoffs, risks, timelines, resource constraints, and next decisions.
Engineering Leadership & Org Building: 10+ years in hardware, software, robotics, or complex systems engineering, including 4+ years managing engineers and 1+ year managing managers, with a track record hiring, scaling, retaining, and developing teams through fast growth and ambiguity.
Operating Discipline: Strong instincts for planning, accountability, risk management, execution reviews, readiness tracking, resource bottleneck detection, and clearing systemic blockers without becoming the technical owner of every detail.
Hardware + Software Systems Context: Real understanding of environments where software/AI, hardware, and real-world deployment are tightly coupled, such as AV, AR/VR, robotics, drones, consumer electronics, smart devices, or AI-enabled manufacturing; enough technical judgment to align specialists without acting as the primary IC.
Research-to-Deployment Fluency: Experience taking research prototypes, complex hardware-software systems, or physical AI products toward dependable real-world deployment.
Cross-Functional Communication: Ability to coordinate clearly with researchers, engineering leads, hardware, operations, deployment, product, field operators, executives, and customers, translating leadership direction into execution-team context without losing ownership, urgency, or clarity.
Relevant Backgrounds: Robotics is a strong plus, but not required. Strong candidates may come from AV, AR/VR, drones, consumer electronics, smart devices, hardware + software platforms, AI-enabled manufacturing, or similar complex hardware-software environments.
Org Scaling: Experience scaling an engineering organization through multiple layers of management.
Field Feedback Loops: Experience building deployment-feedback loops where real-world data directly improves research, models, product, or platform reliability.
Technical Exposure: Exposure to ML training infrastructure, model serving, data systems, real-time systems, edge deployment, hardware-software interfaces, or complex physical-product launches.
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.