Software Engineer (AI)

Location: Global - Remote; United States

Base salary: $xxx,000 - $xxx,000 plus equity

Company

hirify.global builds datasets, reinforcement learning environments, research benchmarks, and agentic AI systems for frontier AI labs and enterprise workflows.

What you will do

• Design and ship end-to-end features across data models, APIs, background workers, and user interfaces.
• Build evaluation workflows for agents and models, including sandboxed trials, scoring, and aggregation of large-scale results.
• Improve benchmark task quality through verifiers, rubric-based grading, and LLM-as-a-judge evaluation.
• Integrate models, agents, and benchmarks using generalized designs rather than one-off special cases.
• Improve performance and reliability through efficient queries, safe migrations, and concurrency that withstands load.
• Review pull requests and maintain consistent engineering standards.

Requirements

• 5+ years of experience building and operating production backend systems, including ownership of at least one service from design through on-call.
• Bachelor’s degree or higher in Software Engineering, Computer Science, or a related field.
• Strong Python experience with Django or a similar framework, PostgreSQL, and a task queue such as Celery.
• Hands-on LLM evaluation experience beyond calling a model API, including benchmark construction, agent-trial scoring, and result aggregation and comparison.
• Ability to identify ambiguous instructions, weak or gameable verifiers, leaked answers, and checks that pass for the wrong reason.
• Ability to make minimal, well-reasoned designs, extend existing systems, and explain tradeoffs.
• Ownership of implementation through migrations, tests, and user-facing edge cases.
• Ability to quickly adopt team conventions and produce clear pull request descriptions and design notes.
• Comfort working with TypeScript and React when changes are visible in the frontend; frontend specialization is not required.

Nice to have

• Experience running agents in sandboxed or containerized environments such as Docker, Kubernetes, Modal, or Daytona.
• Experience with LLM-as-a-judge or rubric-based grading, including reducing judge bias and noise.
• Familiarity with Claude Code, Codex, OpenHands, or MCP.
• Experience with distributed job scheduling, concurrency limits, or database locking at scale.
• Contributions to open-source evaluation or infrastructure projects.

Culture & Benefits

• Written engineering guidelines are applied consistently, with lint and tests required before pull requests are ready.
• Business logic is kept in services, hot paths are kept efficient, and simple designs are preferred over clever ones.
• Work on frontier AI research, expert datasets, reinforcement learning environments, and advanced benchmarks.
• Opportunities to contribute to AI research and showcase work at conferences including ICLR, ICML, and NeurIPS.
• Collaborate with experienced AI and technology professionals while working at startup speed.
• Equal opportunity workplace committed to a diverse, inclusive, and authentic environment.