Engineering Manager, AI

Lattice Lattice · Enterprise · Remote · Engineering

Engineering Manager for the AI Intelligence Quality (IQ) team, responsible for building and leading the AI Platform that enables other AI teams to measure quality, detect regressions, run experiments, and improve AI performance. The role involves leading, coaching, and growing a team of AI and software engineers, owning the AI Platform roadmap, and partnering with various stakeholders to define AI quality measurement and evaluation.

What you'd actually do

  1. Lead, coach, and grow a high-performing team of AI and software engineers, developing them into strong technical leaders while fostering a culture of ownership, technical excellence, experimentation, and continuous learning.
  2. Own the execution and delivery of the AI Platform roadmap, balancing near-term product impact with long-term platform investments.
  3. Partner closely with Product, Applied AI, Data Science, and engineering leaders to define how AI quality is measured, evaluated, and continuously improved across Lattice.
  4. Lead the development of AI evaluation infrastructure, quality metrics, experimentation capabilities, observability, and developer tooling that enable every AI product team to confidently build, evaluate, and ship AI experiences.
  5. Drive technical and organizational decisions that improve the scalability, reliability, and adoption of the AI Platform, empowering engineers to own architecture and technical solutions.

Skills

Required

  • Experience leading and growing high-performing software engineering teams
  • Experience hiring, coaching, and developing engineers into strong technical leaders
  • Strong technical background leading teams that build AI infrastructure, AI-powered products leveraging Large Language Models (LLMs) and/or AI agents, distributed systems, or developer platforms
  • Highly technical engineering leader who leads by example—contributing to architecture, design reviews, and code reviews, diving into implementation details when needed, and raising the technical bar across the team
  • Experience building enterprise SaaS AI products where quality, trust, reliability, and scalability are critical to customer success
  • Proven ability to lead complex cross-functional initiatives from strategy through execution
  • Excellent communication and stakeholder management skills
  • Ability to create clarity, align teams, and influence technical direction in fast-moving, ambiguous environments
  • Strong product and platform mindset
  • Leadership style grounded in curiosity, humility, ownership, and continuous learning

Nice to have

  • Experience building AI evaluation frameworks, experimentation platforms, or ML infrastructure
  • Knowledge of statistical experimentation, A/B model testing, offline evaluations, or model benchmarking
  • Familiarity with advanced AI optimization techniques such as Direct Preference Optimization (DPO), Reinforcement Learning from Human Feedback (RLHF), model fine-tuning, or preference learning
  • Experience building internal platforms and services adopted across multiple engineering teams

What the JD emphasized

  • AI Platform
  • AI quality
  • AI evaluation infrastructure
  • AI performance
  • AI product teams
  • AI infrastructure
  • AI-powered products
  • enterprise SaaS AI products
  • AI evaluations

Other signals

  • AI Platform
  • AI quality
  • AI evaluation infrastructure
  • AI performance
  • AI product teams