Member of Technical Staff, Digital World Engineer
San Jose$170k - $400kfull-timePosted 3w ago
About the role
Lead the development of agentic reinforcement learning environments and the infrastructure behind an agent gym to simulate interconnected digital worlds for training and evaluating AI agents at scale.
Responsibilities
- Build agent environments and simulations end-to-end, including frontend interfaces, backend services, APIs, data models, tools, and realistic workflows used to train and evaluate AI agents.
- Build the infrastructure that powers our agent gym, including orchestration, sandboxing, packaging, benchmarking, and systems for running environments across heterogeneous targets.
- Create interconnected simulations with coherent behavior and shared state across websites, apps, and artifacts. Explore dynamic environments with changing content, background activity, and events over time.
- Partner with researchers to integrate environments into RL training and evaluation pipelines, building reusable agent interfaces, environment lifecycle APIs, and reliable collection of trajectories and artifacts.
- Build infrastructure that supports thousands of concurrent sandboxed environments, with isolation between episodes, consistent state, snapshots and resets, and recovery from partial failures.
- Improve startup and teardown latency, scheduling, resource utilization, and throughput to make large-scale RL rollouts efficient and cost-effective.
- Build observability, debugging, and reproducibility tools. Diagnose resource contention, timeouts, and runtime failures, and distinguish infrastructure problems from agent behavior.
Qualifications
- Strong full-stack engineering skills, with experience building applications across frontend interfaces, backend services, APIs, and data models.
- Experience with stateful backend or distributed systems, including databases, queues, caching, concurrency, consistency, and failure recovery.
- Strong algorithms and systems fundamentals. You can profile complex workloads, identify bottlenecks, and validate performance improvements without compromising correctness.
- Ability to design clear, reusable APIs and abstractions that make complex environments easy for researchers and engineers to integrate, extend, and operate.
- Careful debugging and testing habits, with attention to application fidelity, state transitions, reproducibility, and failures that can silently corrupt results.
- Creativity and ownership in ambiguous problems. You can turn real software behavior into working simulations, collaborate closely with researchers, and learn the agent and RL systems your environments support.
Skills
Required:Full-stack developmentDistributed systems
Last updated Oct 1, 2026. Interested? Apply on the company site.
About Hark
building the most advanced personal intelligence in the world.
At Hark, we are building the most advanced personal intelligence in the world.
personal intelligenceartificial intelligence
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