SWE (RL Environments)

• Location: San Francisco, CA (FiDi)
• Company Stage of Funding: Series A / Hypergrowth AI Startup
• Office Type: On-site (5 days/week in SF)
• Salary: $180,000 – $220,000 Base
• Bonus: Significant cash bonus potential ($200K–$300K+)
• Equity: Competitive
• Visa: Open to H1B transfers, O-1, TN, and STEM OPT

• Our client is building the training data and evaluation infrastructure powering frontier AI labs.
• They work directly with top AI companies including OpenAI, Meta, DeepMind, and other frontier model organizations.
• The company reached $100M ARR in under 18 months and recently raised a $30M Series A.
• They specialize in high-signal datasets, evaluation infrastructure, RLHF/RLVR pipelines, and agentic AI training systems.
• Extremely talent-dense team with backgrounds from Citadel, Palantir, NVIDIA, Databricks, Goldman Sachs, and leading AI startups.
• Small, execution-heavy environment where engineers directly shape how frontier models learn and improve.
• This is an opportunity to work at the frontier of reinforcement learning, evaluation systems, synthetic data, and AI experimentation infrastructure.

What You Will Do

• Build reinforcement learning environments used to train and evaluate frontier AI systems
• Design datasets and evaluation rubrics that expose meaningful model failure modes
• Develop RLHF and RLVR reward signals and experimentation frameworks
• Create scalable pipelines for real-world and synthetic data generation
• Build quantitative frameworks for measuring dataset quality, diversity, and downstream model impact
• Design simulations and environments across domains like coding, finance, enterprise workflows, and reasoning
• Partner directly with frontier AI lab researchers on training objectives and evaluation methodologies
• Rapidly prototype and ship experimental infrastructure and tooling
• Diagnose model weaknesses and develop environments that improve model capabilities
• Work on backend-heavy AI infrastructure and experimentation systems
• Develop scalable evaluation and benchmarking systems for agentic AI workflows
• Iterate quickly from hypothesis to production experiments
• Build V1 systems independently with high ownership and minimal process overhead
• Operate in a highly execution-focused startup environment with strong technical intensity

Ideal Candidate Background

• 1–6 years of software engineering experience
• Explicit hands-on experience building reinforcement learning environments
• Strong backend or fullstack engineering background
• Strong Python engineering skills
• Experience building AI infrastructure, evaluation systems, or simulation environments
• Experience with RLHF, RLVR, supervised fine-tuning, or model evaluation workflows
• Strong systems-thinking and quantitative reasoning ability
• Experience building production-quality experimentation or benchmarking frameworks
• Comfortable working across data pipelines, infrastructure, and backend systems
• Experience at high-growth startups, AI companies, quant firms, or research-heavy environments
• Ability to move quickly and operate autonomously in ambiguous environments
• Strong ownership mentality with bias for action and execution
• Comfortable doing difficult, tedious, and highly iterative engineering work
• Strong CS fundamentals and systems engineering capability

Strong Signals

• Explicit RL environment development experience in production
• Experience at RL-focused AI startups or evaluation infrastructure companies
• Experience building simulations, benchmark systems, or agentic AI evaluation frameworks
• Strong side projects, published AI papers, or open-source contributions
• Experience with RLHF, RLVR, synthetic data, or alignment tooling
• Background from top AI startups, quant firms, or elite engineering organizations
• Experience building fast experimental systems with strong iteration speed
• Experience with data quality measurement and evaluation metrics
• Strong backend engineering depth combined with AI systems exposure
• Experience working directly with researchers or model training teams
• Founder or early startup engineering experience
• Experience building complex AI infrastructure from scratch
• Track record of exceptional execution speed and technical ownership
• Top-tier university background in CS, engineering, math, or related fields

Compensation and Benefits

• Base salary: $180,000 – $220,000
• Significant uncapped performance bonus potential
• Competitive equity package
• Opportunity to work directly with frontier AI labs
• Highly technical and talent-dense engineering environment
• Massive ownership and impact on core AI systems
• Exposure to cutting-edge RL, evaluation, and AI training infrastructure
• Extremely fast-moving startup environment with rapid career growth
• Ability to shape foundational infrastructure for next-generation AI systems

Why Join

• This is one of the highest-leverage engineering opportunities in frontier AI infrastructure today.
• You’ll work directly on the systems that determine how advanced models are evaluated, trained, and improved.
• The company is scaling rapidly with elite customers, elite talent density, and strong product-market fit.
• If you enjoy reinforcement learning environments, evaluation systems, AI infrastructure, fast execution, and operating close to frontier model development, this role offers exceptional technical scope and upside.