Research Engineer (AI)

TL;DR

Research Engineer (AI/ML): Implementing and improving frontier ML methods and benchmarks to build next-generation agent capabilities with an accent on RL, agentic systems, and multimodal ML. Focus on turning ambiguous research directions into concrete MVPs, datasets, and publications.

Location: Hybrid (San Francisco, US)

Company

hirify.global works at the intersection of frontier AI research and production impact, collaborating with global AI labs to enhance model performance.

What you will do

• Independently identify and implement methods from research papers, benchmarks, and blog posts.
• Own the end-to-end process of reproducing and improving prior ML research internally.
• Build RL/agentic environments and novel multimodal benchmarks.
• Translate ambiguous requirements into concrete, testable research plans.
• Validate ideas through hands-on implementation, data annotation, and evaluation.
• Produce tangible outputs such as customer datasets, pilots, or conference publications.

Requirements

• MS or PhD in ML, CS, or a related quantitative field (or equivalent demonstrated research experience).
• Deep understanding of how ML models are trained and evaluated.
• Hands-on experience with RL, agentic systems, AI/ML benchmarking, or multimodal ML.
• Strong Python engineering skills for building eval harnesses and infrastructure.
• Must be based in San Francisco for a hybrid work arrangement.
• Ability to produce clear and professional technical writing.

Nice to have

• Proven publication track record (preferably as first author).
• Experience with benchmarks like OSWorld, MMMU, WebArena, or SWE-bench.
• Familiarity with reward modeling, reward hacking, or verifier/judge reliability.
• Experience with synthetic data generation or human-in-the-loop (HITL) workflows.
• Knowledge of cloud infrastructure and containerized environments.

Culture & Benefits

• Work on high-impact projects with frontier AI labs.
• Opportunity to contribute directly to next-generation agent capabilities.
• Ability to publish research and present at major AI conferences.
• Collaboration within a multidisciplinary and multinational team.