ML Research Scientist, Environments & Harnesses

Location: Cambridge, MA, USA; London, UK; San Francisco, CA, USA. Other locations include Alewife, Cambridge, MA; Europe; and San Francisco, CA.

Expected base salary range: $228,000–$304,000 USD annually for U.S.-based positions; international salaries are set to local market.

Company

hirify.global is building AI systems and proprietary instruments for autonomous scientific discovery across medicine, materials, and energy.

What you will do

• Design and prototype harness capabilities including context compaction, tool interfaces, long-horizon planning, and multi-agent swarms.
• Invent reinforcement learning environments from scientific workflows, simulators, and real-world tasks.
• Build scalable end-to-end pipelines that transform papers, codebases, and lab protocols into validated, versioned training environments.
• Prototype and validate environments through reward shaping, data collection, and training-loop integration.
• Design support for chained tasks, hierarchical goals, multi-step reasoning, and complex evaluation scenarios.

Requirements

• PhD or equivalent experience in Computer Science or a related quantitative field, plus two years of industry experience.
• Strong track record of ideating on harness designs or creating reinforcement learning environments at scale.
• Strong prototyping and coding skills, with the ability to quickly build and iterate in Python.
• Creativity, research intuition, experiment-design ability, and comfort navigating ambiguity.
• Familiarity with core reinforcement learning concepts and algorithms.
• Solid software engineering fundamentals, including clean code, version control, testing, and integration of experimental work into shared infrastructure.

Nice to have

• Strong publication record at conferences such as NeurIPS, ICML, or ICLR.
• Background in biology, chemistry, or physics, or experience translating domain expertise into computational tasks.
• Experience building developer-facing libraries, APIs, or frameworks such as registries, plugin systems, or CLIs.
• Familiarity with distributed compute tools such as Ray or Modal, or LLM inference systems including vLLM and batching strategies.
• Experience with async Python, Pydantic, and workflow orchestration frameworks such as Flyte or Airflow.

Culture & Benefits

• Full-time U.S. employees receive medical, dental, and vision coverage, employer-paid life and disability insurance, paid parental leave, and flexible time off with company-wide holidays.
• Benefits also include educational assistance, commuter benefits including bike-share memberships for office-based employees, and a company-subsidized lunch program.
• Full-time employees outside the U.S. receive benefits tailored to their region.
• Compensation includes potential bonuses and early-stage equity.
• The company operates with startup speed and values truth, trust, curiosity, grit, and velocity.

Hiring process

• Applications are accepted from candidates directly; unsolicited agency resumes are not accepted unless the agency is contacted by LILA's internal Talent Acquisition team.