
Member of Technical Staff, Digital World Engineer
About this role
The Member of Technical Staff, Digital World Engineer position at Hark in San Jose is an exciting opportunity for a skilled developer to build foundational agentic reinforcement learning environments. This role suits professionals with strong full-stack and systems engineering expertise who want to shape the future of artificial intelligence hardware and software.
Role Overview
In this capacity, candidates will lead the creation of digital worlds where artificial intelligence agents learn software navigation, complex workflows, and environmental interaction. The work involves designing end-to-end simulations that mimic the complexity of the internet, featuring interconnected websites, apps, and services. Practitioners will collaborate alongside researchers to integrate these digital gyms into reinforcement learning training pipelines.
Key Responsibilities
Requirements
Nice to Have
Skills and Tools
Salary and Benefits
About the Company
Hark develops advanced, proactive, and multimodal artificial intelligence designed to interact through speech, text, vision, and persistent memory. By pairing this intelligence with next-generation hardware, the organization creates a unified universal interface between humans and machines.
How to Apply
Interested candidates should visit the official company careers page to submit an application for this position.
Questions candidates ask
What is the location of this job?
This position is located in San Jose.
What is the salary range for the Member of Technical Staff role?
The annual US base salary range spans from $170,000 to $400,000, varying based on individual factors like experience and knowledge.
What department does this role belong to?
This position belongs to the Computer Use Agents department at Hark.
What are the primary responsibilities of a Digital World Engineer?
Responsibilities include building end-to-end agent simulations, developing agent gym infrastructure, scaling concurrent sandboxed environments, and partnering with researchers for reinforcement learning integration.
What qualifications are considered a bonus for applicants?
Bonus qualifications include experience integrating environments with reinforcement learning pipelines, working with container or sandbox runtimes, and familiarity with browser automation or computer-use agents.