Tech Lead Manager, AI/ML

Numerator is looking for a hands-on Tech Lead Manager to join our growing Machine Learning team. This is a 50/50 player-coach role: you'll directly manage a small team of ML engineers while continuing to write code, design systems, and ship GenAI features yourself. You'll work with an established and rapidly evolving platform that handles millions of requests and massive data volumes, and you'll be responsible for both the team's technical direction and the growth of the people on it.

Historically, our team has focused on reducing COGS through ML automation. That work continues — and we're now also building agentic experiences for clients and internal stakeholders. You'll help shape both the technical roadmap and how the team operates as we expand into this space. This is an established role due to internal growth.

How You'll Spend Your Time:

• Manage and grow a small team of AI software engineers — 1:1s, career development, performance, hiring, and day-to-day unblocking

• Stay deeply technical: contribute to the codebase, design GenAI systems, and own meaningful slices of delivery alongside your team

• Apply agentic LLM patterns (tool use, multi-step reasoning, orchestration) to automate high-complexity tasks that previously required human judgment

• Design and build GenAI-powered solutions for complex NLP tasks — NER, classification, information retrieval, summarization, and structured output generation

• Partner closely with the team's PM and adjacent engineering teams to scope, prioritize, and ship

• Translate ambiguous business problems into well-scoped technical work — and help your engineers learn to do the same

• Stay current with the fast-moving GenAI landscape and translate new capabilities into practical team impact

• 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

Extra, nice to haves

• Experience managing engineers working across both traditional ML and GenAI

• Experience with agentic orchestration frameworks

• Fine-tuning experience with modern techniques — especially applied to domain adaptation for NLP tasks

• PyTorch or Hugging Face familiarity

• Familiarity with LLM evaluation frameworks and a structured approach to measuring model quality

• Inference optimization awareness — understanding latency/cost/accuracy tradeoffs for LLM solutions

• Experience building and deploying robust machine learning APIs in cloud environments (AWS or GCP)

What We Offer

• An inclusive and collaborative company culture- we work in an open environment while working together to get things done, and adapt to the changing needs as they come.

• Market competitive total compensation package.

• Volunteer time off and charitable donation matching.

• Strong support for career growth, including mentorship programs, leadership training, access to conferences and employee resource groups.

• 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

Extra, nice to haves

• Experience managing engineers working across both traditional ML and GenAI

• Experience with agentic orchestration frameworks

• Fine-tuning experience with modern techniques — especially applied to domain adaptation for NLP tasks

• PyTorch or Hugging Face familiarity

• Familiarity with LLM evaluation frameworks and a structured approach to measuring model quality

• Inference optimization awareness — understanding latency/cost/accuracy tradeoffs for LLM solutions

• Experience building and deploying robust machine learning APIs in cloud environments (AWS or GCP)

What We Offer

• An inclusive and collaborative company culture- we work in an open environment while working together to get things done, and adapt to the changing needs as they come.

• Market competitive total compensation package.

• Volunteer time off and charitable donation matching.

• Strong support for career growth, including mentorship programs, leadership training, access to conferences and employee resource groups.

There is strength in numbers - We are the Numerati

Numerator is 5,800 employees strong. We have the confidence to be real and embrace what makes each Numerati unique. Our diverse experiences, ideas and backgrounds fuel our innovation.

Being part of the Numerati means that we’ll take care of you! From our Recharge Days, maximum flexibility policy, wellness resources for employees and their families, development opportunities and much more — we’re always finding ways to better support, celebrate and accelerate our team.