
Job Overview
Location
San Francisco Office, California
Job Type
Full-time
Category
Software Engineering
Date Posted
June 4, 2026
Full Job Description
đź“‹ Description
- • Lead and develop the engineering team responsible for the RL environment and human-data infrastructure that powers frontier AI models, working closely with two existing team leads to elevate performance and accelerate delivery.
- • Set the technical direction and quality bar for scalable systems that process expert human judgment into clean training signals for LLMs, ensuring data integrity as volume grows.
- • Serve as the primary engineering liaison for Research, Applied AI, and Operations teams, aligning engineering capacity with cross-functional needs and maintaining an honest, realistic roadmap.
- • Own hiring and headcount planning in a highly competitive talent market, prioritizing quality over speed and ensuring the team’s capacity is accurately assessed and respected.
- • Coach and develop engineering managers and tech leads into the next layer of leadership, fostering a culture where engineers stay, grow, and thrive despite industry-wide retention challenges.
- • Actively participate in incident response; remain technically fluent enough to review code, push back on architecture decisions, and provide credible feedback on engineering work—even if not writing daily commits.
- • Drive AI-native execution as the default mode of operation across the team, embedding coding agents and automated workflows into daily processes rather than treating them as experimental side projects.
- • Maintain a deep, credible understanding of LLM post-training, evals, RL environments, and agent systems, offering strategic insight into where the field is headed and how infrastructure must evolve.
- • Balance speed with quality: prioritize clean training signals over rapid delivery, recognizing that corrupted data is more damaging than delayed features.
- • Operate in a fast-paced, adaptive environment where planning cycles are measured in weeks, not quarters, and roadmaps evolve in real time alongside frontier AI breakthroughs.
- • Embrace full ownership of strategy, architecture, and execution—no clean handoffs between phases; you are accountable for the entire lifecycle of engineering outcomes.
- • Work hybrid in the San Francisco office, with a minimum of two days per week required in-person to foster collaboration and team cohesion.
- • Report directly to the CEO and founder, Phoebe Yao, and influence the technical trajectory of a platform already in production and used by leading frontier labs like Anthropic and GDM.
🎯 Requirements
- • Managed an engineering team of meaningful size, including managers or strong tech leads, through periods of rapid growth
- • Remain technically fluent: able to review PRs, assess architecture, and contribute meaningfully during incidents without being a bystander
- • Proven track record of retaining top engineers and developing managers in a competitive talent market
- • Deep, credible understanding of LLMs, RL environments, evals, and post-training methodologies
- • Experience working in a fast-moving, adaptive environment where planning cycles are short and roadmaps shift frequently
- • Willingness and ability to work hybrid in San Francisco, minimum two days per week in office
🏖️ Benefits
- • Base salary range of $260,000 to $310,000
- • Equity compensation as part of total offer
- • Opportunity to shape the engineering backbone of AI infrastructure used by frontier labs like Anthropic and GDM
- • Seat and influence that grows as the company scales
Skills & Technologies
About Pareto AI, Inc.
Pareto AI, Inc. develops data-science software that automates lead research and outbound sales targeting for B2B companies. Its platform aggregates public and proprietary datasets, applies machine-learning models to identify high-intent prospects, and delivers ranked lead lists directly to CRMs. Customers configure ideal customer profiles and receive continuously refreshed contacts, firmographics, and buying signals, reducing manual research time and improving campaign conversion rates. The company serves SaaS, fintech, and professional-services teams seeking scalable, data-driven pipeline growth.
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