Tech Lead Manager, AI / Machine Learning

What You'll Bring to Numerator

• 2+ years of engineering or data science management experience — 4+ direct reports, performance conversations, hiring.
• 4+ years of hands-on ML or GenAI engineering experience, including production systems
• Strong practical GenAI fundamentals: LLM APIs, context engineering, RAG, tool/function calling, agents, and evaluation methodology — you understand why these techniques work, not just how to call them
• Technical judgment: you can scope ambiguous problems, make sound build-vs-buy and custom-vs-off-the-shelf calls, and balance shipping speed with long-term maintainability
• Data acumen: you can critically assess a dataset, spot distribution problems, and reason about how data quality affects downstream model and business outcomes
• Product orientation: you engage with business context naturally, partner with PM as an equal, and translate ambiguous requirements into well-scoped technical solutions
• A people-first management style: you grow engineers through coaching and stretch work, give direct and timely feedback, and create the conditions for your team to do their best work
• Solid Python and software engineering fundamentals — clean, testable code, REST API design, debugging, and familiarity with CI/CD
• A genuine habit of self-improvement — you follow the field actively, experiment with new models and tools, and bring what's relevant back to the team

Experience

• Nice to have: Experience managing engineers working across both traditional ML and GenAI
• Nice to have: Experience with agentic orchestration frameworks
• Nice to have: Fine-tuning experience with modern techniques — especially applied to domain adaptation for NLP tasks
• Nice to have: PyTorch or Hugging Face familiarity
• Nice to have: Familiarity with LLM evaluation frameworks and a structured approach to measuring model quality
• Nice to have: Inference optimization awareness — understanding latency/cost/accuracy tradeoffs for LLM solutions
• Nice to have: Experience building and deploying robust machine learning APIs in cloud environments (AWS or GCP)