Technical Lead (Agentic AI)

Location: On-site in San Francisco, United States

Base salary: $250,000–$325,000 USD annually, plus 0.15%–0.30% equity.

Company

hirify.global builds AI employees that take on real work across sales and other roles and industries.

What you will do

• Design and implement agent execution loops, planning strategies, tool interfaces, verification, and clear system boundaries.
• Build multi-agent delegation, coordination, context sharing, result synthesis, and parallel execution.
• Make stateful workflows resilient through checkpoints, recovery strategies, retries, cancellation, and human intervention.
• Improve retrieval, context construction, persistent memory, reusable skills, and feedback-driven system behavior.
• Build evaluations, analyze task trajectories, compare approaches, and turn failures into measurable improvements.
• Own AI architecture decisions, evaluate models and tools, set engineering standards, and collaborate with product and infrastructure engineers on production services, integrations, secure execution, and observability.

Requirements

• Personally built and shipped a substantial agentic system in production or through rigorous, reproducible open-source work.
• Deep practical experience with LLM tool use, planning, context engineering, evaluations, and multi-agent coordination or substantial parallel agent and tool execution.
• Hands-on experience with browser or computer automation, including state observation, effect verification, and recovery from failures.
• Strong software engineering skills in Python, TypeScript, or a comparable language, with experience in asynchronous services, state machines, persistence, concurrency, retries, and cancellation.
• Ability to reason about permissions, untrusted inputs, uncertain external outcomes, human approvals, and which guarantees must be enforced in code.
• Ability to take ownership of ambiguous technical problems, design meaningful experiments, debug system behavior, and ship reliable software.

Nice to have

• Depth in agent memory and retrieval, skill acquisition, reinforcement learning or post-training, trajectory datasets, sandboxed execution, distributed systems, inference optimization, or multimodal and voice models.
• Experience with relevant agent frameworks, open-source contributions, technical write-ups, or project links.

Culture & Benefits

• Small engineering team with short feedback cycles, direct communication, and high technical standards.
• Substantial freedom to explore ambitious technical ideas and responsibility for turning them into working software.
• Full-time, in-person work from the San Francisco office.
• US visa sponsorship available.
• Equity participation of 0.15%–0.30%.

Hiring process

• Discuss systems personally built, including one owned agentic system, its hardest failure, and measured improvements.
• Complete a practical agent architecture and debugging exercise.
• Discuss collaboration and ways of working with the team.