Engineering Manager (AI)

TL;DR

Engineering Manager (AI): Leading the development of an AI Platform for evaluating quality, detecting regressions, running experiments, and improving AI performance with an accent on evaluation infrastructure, observability, and developer tooling. Focus on building scalable platform capabilities, guiding architecture and implementation, and establishing reliable AI quality practices across multiple product teams.

Location: Remote; applications accepted from anywhere, including Latin America

Salary: $187,500–$234,500 annual cash compensation, plus eligibility for incentive stock options.

Company

A people success SaaS platform providing performance management, employee engagement, feedback, goal-setting, and career development tools.

What you will do

• Lead, coach, hire, and grow a high-performing team of AI and software engineers.
• Own execution and delivery of the AI Platform roadmap while balancing product impact with long-term platform investments.
• Partner with Product, Applied AI, Data Science, and Engineering leaders to define and improve AI quality measurement.
• Lead development of AI evaluation infrastructure, quality metrics, experimentation capabilities, observability, and developer tooling.
• Drive technical and organizational decisions that improve platform scalability, reliability, adoption, and developer ownership.
• Establish AI evaluations as a foundational capability across product engineering teams.

Requirements

• Experience leading and growing software engineering teams, including hiring, coaching, and developing technical leaders.
• Strong technical background in AI infrastructure, LLM- or AI-agent-powered products, distributed systems, or developer platforms.
• Hands-on leadership in architecture, design reviews, code reviews, and implementation-level technical decisions.
• Experience building enterprise SaaS AI products where quality, trust, reliability, and scalability are critical.
• Proven ability to lead complex cross-functional initiatives from strategy through execution.
• Excellent communication, stakeholder management, product thinking, and platform mindset.

Nice to have

• Experience with AI evaluation frameworks, experimentation platforms, or ML infrastructure.
• Knowledge of statistical experimentation, A/B model testing, offline evaluations, or model benchmarking.
• Familiarity with DPO, RLHF, model fine-tuning, or preference learning.
• Experience building internal platforms adopted across multiple engineering teams.

Culture & Benefits

• Medical, dental, vision, life, AD&D, and disability insurance.
• Paid parental leave, paid time off including holidays and sick time.
• 401(k) retirement plan, financial planning, and learning and development budget.
• Internet and phone stipend plus a one-time work-from-home office setup stipend.
• Wellness apps, emergency weather support, commuter and parking accounts, and office lunches.