Software Engineer - RL Environments

Role: Software Engineer – Reinforcement Learning Environments & Evaluation Infrastructure

Company: Confidential (Series A Frontier AI Infrastructure Platform) Location: San Francisco, CA (Hybrid)

Visa Support: Available (O-1 / OPT)

PURPOSE OF THE POSITION

The Software Engineer – RL Environments & Evaluation Infrastructure role is focused on architecting the core training data pipelines and rigorous simulation frameworks that power the world's leading foundation models.

At a high level, this engineer will serve as the critical bridge between raw domain expertise and scalable algorithmic alignment—ensuring that dataset structures, feedback mechanisms, and reward loops are programmatically synthesized to eliminate model vulnerabilities and expand downstream capabilities.

This is not a traditional product engineering or purely theoretical research role. It is a highly specialized infrastructure and systems position focused on building high-signal diagnostic environments where frontier AI models are trained, evaluated, and stress-tested at scale. The engineer will play a pivotal role in designing verifiable reward architectures, modeling complex human-in-the-loop behaviors, and accelerating the empirical experimentation cycle of leading AI research laboratories.

RESPONSIBILITIES

ENVIRONMENT DESIGN & DIAGNOSTICS • Construct targeted data stratifications and simulation slices designed to systematically surface edge cases and behavioral vulnerabilities in foundation models across high-stakes verticals (e.g., quantitative finance, advanced code generation, and multi-step enterprise automation). • Formulate robust, isolated runtime environments to observe, isolate, and log model execution failures under variable constraints. • Translate highly abstract training goals into concrete, programmatic data blueprints and testing environments.

REWARD MODELING & PIPELINE ENGINEERING • Design, implement, and optimize scalable reward signals and heuristic feedback mechanisms powering Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from Verifiable Rewards (RLVR) training pipelines. • Build and manage high-throughput, low-latency data pipelines capable of processing both complex organic interaction data and multi-modal synthetic data streams. • Model and simulate human annotator dynamics to programmatically eliminate bias and optimize the quality of ground-truth training inputs.

QUANTITATIVE FRAMEWORKS & ANALYTICS • Establish mathematical and statistical frameworks to benchmark dataset structural purity, semantic diversity, and direct downstream impact on model reasoning capabilities. • Develop automated pipelines to execute rapid, lightweight data experiments, extracting actionable insights from unstructured or highly volatile inputs. • Monitor, profile, and optimize the cost, compute footprint, and execution reliability of large-scale dataset generation runs.

RESEARCH INTEGRATION & COLLABORATION • Partner directly with tier-one AI research teams to align infrastructure capabilities with next-generation model alignment objectives. • Contribute to internal engineering standards, version control frameworks, and technical documentation for synthetic data generation pipelines.

REQUIRED QUALIFICATIONS

CORE TECHNICAL BACKGROUND • 1 to 4 years of production-grade software engineering experience with undeniable technical depth and a proven track record of shipping stable code in high-velocity environments. • Systems & Data Obsession: A strong engineering conviction that data architecture, structural layout, and filtering design drive model breakthroughs far more effectively than brute-force compute or theoretical parameter tuning. • Advanced Systems Fluency: Exceptional proficiency in Python and SQL, alongside practical experience building automated data pipelines, custom infrastructure tools, or complex backend systems. • Empirical Agility: Demonstrated capability to design lightweight technical experiments, move fast, and isolate signal from messy, highly ambiguous datasets.

PREFERRED & DIFFERENTIATING ATTRIBUTES • Direct engineering experience within Reinforcement Learning simulation environments, advanced AI evaluation consortiums, or specialized benchmarking organizations. • Background as an early engineer, technical co-founder, or core contributor at a high-growth, venture-backed technology startup. • Prior exposure to orchestrating synthetic data generation loops or optimizing RLHF/RLVR training mechanics.

WHAT SUCCESS LOOKS LIKE

• High-Signal Environments: Production of pristine, highly targeted diagnostic datasets that successfully expose and remediate critical model failure modes prior to compute-intensive training runs. • Infrastructure Velocity: Decreased latency between research hypothesis generation and live pipeline experiment execution, enabling continuous iteration on reward functions. • Architectural Precision: Implementation of reliable, well-documented evaluation rubrics providing verifiable visibility into model alignment trajectories and performance metrics.

COMPENSATION & STRUCTURE

• Base Salary Range: $180,000 – $220,000 USD Base • Performance Incentives: Lucrative corporate profit-sharing framework targeted at approximately 150% of base salary, bringing the expected total first-year cash compensation to circa $500,000 USD. • Equity: Highly competitive early-stage equity package. • Structural Benefits: Premium healthcare, direct collaboration with world-class AI labs, and immediate, unblocked ownership over critical systems on the frontier of AI development.