Manager, AI Engineering - Analytics

What you'll do

Build Alongside the Team

• Stay deeply hands-on by writing code, designing systems, and reviewing PRs
• Own critical paths and pair with engineers on the hardest parts of the product
• Keep close to the codebase and the customer experience even as the team grows
• Set the bar for engineering quality through your own work

Lead a Small Team

• Lead a small, focused team of engineers and grow it thoughtfully over time
• Set clear goals, run good 1:1s, and create an environment where engineers do their best work
• Give direct, useful feedback and help engineers grow in their careers
• Invest in the basics of management: hiring, performance, career growth, and team health
• Partner with leadership to grow into the formal management craft

Own the AI and Data Direction

• Set the technical direction for AI-driven analytics and the data foundation underneath it
• Make pragmatic decisions across the stack, from data modeling to agent design
• Define multi-tenant data access patterns that safely serve customer-scoped data at scale
• Make sound build, buy, and adopt decisions for the team's tooling
• Stay current on developments in applied AI and bring relevant ideas back to the team

Build Natural Language Data Experiences

• Help shape and build features that let users ask questions of their data in natural language
• Ground AI responses in real data, handle ambiguity, and surface uncertainty appropriately
• Keep AI-driven experiences fast, accurate, and trustworthy
• Iterate quickly with design partners to find what works in production

Make Evals a First-Class Practice

• Build the evals, telemetry, and offline/online test loops the team relies on
• Establish eval-driven development as the default workflow
• Define what "good" means for each AI feature and measure it rigorously
• Use eval results to guide model, prompt, and architecture decisions

Ship and Learn

• Drive end-to-end delivery from spec to GA
• Partner with Product on scope, sequencing, and tradeoffs
• Ship iteratively to design partners, instrument adoption, and learn from real usage
• Establish the metrics that prove the experience is delivering value

What you'll bring

AI Engineering

• Real AI engineering background with at least one agent or LLM-powered system shipped to production end-to-end
• Working knowledge of prompts, tool use, retrieval, and structured outputs
• Understanding of latency, cost, and quality tradeoffs in LLM-based systems
• Familiarity with the failure modes of AI features in the real world

Evals

• Hands-on experience designing and building evals for AI systems
• Comfort with offline benchmarks, regression testing for non-deterministic systems, and online feedback loops
• Ability to articulate how to evaluate an agent before, during, and after launch
• Bias toward measurable quality over vibes

Data Fundamentals

• Strong SQL skills and comfort with modern data warehouses
• Experience with data modeling and the plumbing that powers analytics
• Ability to reason about query performance, data contracts, and multi-tenant access patterns
• Comfort working close to the data, not just on top of it

Hands-On and Pragmatic

• Happy writing code and intend to keep doing it
• Pragmatic about technology choices and careful about complexity
• Bias toward shipping and learning over over-engineering
• Comfortable working across the full stack on a small team

Ready to Lead

• Track record of leading projects, mentoring engineers, and driving technical direction
• Strong written and verbal communication
• Direct, kind feedback style and a desire to invest in growing a team
• Clear pull toward leadership, even without prior formal management experience

Requirements

• 6+ years of software engineering experience, with at least 2 focused on AI/ML or applied AI work (agents, LLMs, evals, or similar)
• At least one agent or LLM-powered system deployed to production that you owned end-to-end
• Hands-on experience building and using evals to measure and improve AI quality
• Solid data engineering or analytics engineering experience, including SQL, modeling, and modern data warehouses
• Track record of shipping production software on small teams and operating across the full stack
• Experience as a tech lead, project lead, or strong mentor, with a desire to grow into formal management
• Strong written and verbal communication
• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience

Bonus Qualifications

• Prior experience working on a customer-facing data product, embedded analytics, BI tooling, or a natural language interface over structured data (text-to-SQL, conversational analytics, or similar)
• Experience with semantic modeling layers or modern BI infrastructure
• Experience integrating AI agents with structured data sources
• Background in compliance, security, GRC, or other regulated SaaS verticals
• Prior tech lead or team lead experience
• Previous experience at high-growth SaaS companies