RL Environment Engineer for Real Enterprise Data

Sunset; New York (NY); On-site; USD 220,000 - 320,000; Full time

Benefits offered by this job

Meaningful equity

Medical, dental & vision

Unlimited PTO

Gym memberships

Desk allowance

Relocation bonus

Job summary

Replay is seeking an RL Environment Engineer to turn real enterprise data into production‑grade training environments for frontier AI labs. You will design tasks, tools, and evaluators, partnering with researchers to close capability gaps and ensure reproducible, safe rollout of environments.

The role demands shipping ownership, strong software engineering, and experience building environments used for real training/evaluation, with a focus on robustness and measurable signal quality.

Qualifications

• 3-5+ years of ML / software engineering experience shipping production systems.
• Built production‑grade RL environments that were used in real training or evaluation, not just prototypes.
• Ability to turn ambiguous goals from researchers into well‑scoped, working systems.
• A clear grasp of environment design tradeoffs: task specification, state and tool design, determinism, difficulty calibration and solvability.
• Built evaluation or agentic harnesses, with rigor: precision, recall, calibration, distribution shift.
• High ownership, action‑oriented mindset: take problems from idea to shipped.
• Startup experience and comfort with evolving requirements.
• Clear written and verbal communication with researchers, engineers, and non‑technical partners.]
• job_responsibilities_description ["Design and build production‑grade RL environments grounded in real enterprise workflows, from task spec through to a stable, versioned release","Write reward functions and programmatic verifiers that score outcomes reliably, and harden them against reward hacking and shortcut solutions","Build high‑fidelity evaluation environments with seeded failure modes that expose subtle regressions, and reliable model and agent harnesses with bounded behaviour and explicit output verification","Turn Replay's de‑identified data into realistic state, tools, files and context for an agent, and keep environments reproducible, observable and safe to roll out, with clear evidence of what changed between versions","Work directly with frontier lab researchers to understand capability gaps and design task suites that expose and train against them","Measure task difficulty and solvability by running frontier models against environments, then tune them until they give a usa
• CoT_job_summary_short":"COMPANY NAME: Replay\nKEY POINTS: Production RL environments, real enterprise data, ownership mindset"
• job_summary_short":"<p>Replay is seeking an RL Environment Engineer to turn real enterprise data into production‑grade training environments for frontier AI labs. You will design tasks, tools, and evaluators, partnering with researchers to close capability gaps and ensure reproducible, safe rollout of environments.</p><p>The role demands shipping ownership, strong software engineering, and experience building environments used for real training/evaluation, with a focus on robustness and measurable signal quality.</p>
• contract_type(
• fulltime
• location_type
• on site
• remote_scope
• null
• perks
• [{"name":"Meaningful equity"},{"name":"Medical, dental & vision"},{"name":"Unlimited PTO"},{"name":"Gym memberships"},{"name":"Desk allowance"},{"name":"Relocation bonus"}]
• stated_min
• 220000
• stated_max
• 320000
• stated_absolute
• null
• salary_estimation_cot
• RL Environment Engineer in New York: base salary around 220k–320k USD with meaningful equity.
• estimated_low
• 220000
• estimated_medium
• 270000
• estimated_high
• 320000
• currency
• USD
• frequency
• yearly
• bonus
• null
• tax
• gross
• application_email
• null
• job_end_date
• null
• contact_person
• null
• hiring_department_name
• null

Responsibilities

• Design and build production‑grade RL environments grounded in real enterprise workflows, from task spec through to a stable, versioned release
• Write reward functions and programmatic verifiers that score outcomes reliably, and harden them against reward hacking and shortcut solutions
• Build high‑fidelity evaluation environments with seeded failure modes that expose subtle regressions, and reliable model and agent harnesses with bounded behaviour and explicit output verification
• Turn Replay's de‑identified data into realistic state, tools, files and context for an agent, and keep environments reproducible, observable and safe to roll out, with clear evidence of what changed between versions
• Work directly with frontier lab researchers to understand the capability gaps they want to close, and design task suites that expose and train against them
• Measure task difficulty and solvability by running frontier models against environments, then tune them until they give a usable training signal
• Ship quickly, and help set the standards and patterns for how Replay builds tasks and environments

Skills

ML / software engineering

Production‑grade RL environments

Turn researchers' goals into systems

Environment design tradeoffs

Evaluation harnesses / rigor

Ownership mindset

Startup experience

Communication with researchers & peers

Tools

RL environments tooling

Job description

Replay is seeking an RL Environment Engineer to turn real enterprise data into production‑grade training environments for frontier AI labs. You will design tasks, tools, and evaluators, partnering with researchers to close capability gaps and ensure reproducible, safe rollout of environments.

The role demands shipping ownership, strong software engineering, and experience building environments used for real training/evaluation, with a focus on robustness and measurable signal quality.