Research Engineer

Research engineers at OpenBlock Labs build advanced agent-based reinforcement learning systems that use blockchains as verifiable, transparent environments for training AI. This involves solving novel challenges at the intersection of AI agents, reinforcement learning, and blockchain-based verification.

As a Research Engineer, you'll be at the forefront of a new paradigm in agent training—designing and implementing systems that leverage blockchain's inherent transparency and verifiability to create more capable, trustworthy autonomous agents.

Sample projects include

• Developing reinforcement learning frameworks that use blockchain-verified environments for agent training
• Creating verifiable reward mechanisms that align agent behavior with desired outcomes
• Building autonomous agents that can analyze on-chain data and make informed decisions
• Designing systems that transform blockchain protocols into transparent training grounds for AI agents

You might be a fit if

• You have experience with reinforcement learning, agent-based systems, or AI training methodologies
• You're comfortable working with modern AI frameworks and blockchain technologies
• You can design and implement systems that bridge the gap between AI and blockchain verification
• You're passionate about creating autonomous agents that leverage verifiable training environments

Compensation

This role offers a base salary range of $200,000-$320,000, plus meaningful equity grants that represent true ownership in our vision. As a key early member, you'll receive substantial upside potential tied to our growth and success.

Additionally, the role involves:

• In-depth exploration of your expertise and problem-solving approach
• A day in our SF office solving an interesting problem with the team

Our interview process is focused and collaborative. We start with a brief conversation, followed by a technical discussion about your expertise. The final step is a hands-on day in our San Francisco office where you'll solve an interesting problem alongside the team – a chance for us both to see what working together would be like.