Tech Lead Manager, AI/ML

Company Overview

Numerator is revolutionizing the market research sector by delivering cutting-edge market intelligence that empowers top brands and retailers with unparalleled insights into consumer behavior and influential factors.

Role Overview

Numerator seeks a proactive Tech Lead Manager to join their expanding Machine Learning team. This hybrid role blends leadership duties with hands-on engineering: managing a small group of ML engineers while actively coding, designing systems, and deploying Generative AI (GenAI) features. The position involves working with a mature, high-throughput platform processing millions of requests and large datasets, alongside steering both technical directions and talent development within the team.

Responsibilities

• Lead and nurture a small AI engineering team through regular one-on-ones, performance evaluations, hiring, and day-to-day support.
• Maintain active technical involvement, contributing directly to codebases and delivering significant GenAI system components.
• Implement agent-based large language model (LLM) strategies—including tool integration, multi-step reasoning, and task orchestration—to automate complex human-judgment tasks.
• Architect and develop GenAI-driven solutions addressing challenging NLP tasks such as named entity recognition, classification, information retrieval, summarization, and structured output generation.
• Closely collaborate with product management and adjacent engineering teams to define scope, prioritize initiatives, and deliver results.
• Convert unclear business challenges into well-defined technical projects, mentoring engineers to develop similar translation skills.
• Stay updated on evolving GenAI technologies and translate new advancements into practical applications benefiting the team.

Qualifications

• Minimum 2 years managing engineering or data science teams, including overseeing 4+ direct reports and conducting hiring and performance discussions.
• At least 4 years of direct experience building and maintaining production ML or GenAI systems.
• Deep understanding of GenAI fundamentals such as LLM APIs, context engineering, retrieval-augmented generation (RAG), tool and function integration, agent frameworks, and evaluation techniques—comprehending underlying principles, not just usage.
• Excellent technical decision-making skills, capable of defining project scopes, balancing build vs. buy options, and managing trade-offs between rapid delivery and sustainable architecture.
• Strong data literacy: able to critically evaluate datasets, identify distributional issues, and understand the implications of data quality on models and business outcomes.
• Product-centric mindset with the ability to engage with business contexts, partner effectively with product managers, and clarify ambiguous requirements into actionable technical designs.
• A managerial approach focused on people development: coaching engineers, providing candid feedback, and fostering an environment where the team thrives.
• Solid expertise in Python and software engineering practices, including writing clean, testable code, designing RESTful APIs, debugging, and familiarity with CI/CD pipelines.
• A commitment to continuous learning by staying current with new models and tools and integrating relevant innovations back into the team.

Preferred Additional Experience

• Leadership of engineers working with both traditional ML methods and cutting-edge GenAI.
• Knowledge of agent orchestration frameworks.
• Experience fine-tuning models, especially for domain-specific NLP adaptations.
• Familiarity with PyTorch or Hugging Face libraries.
• Understanding of LLM evaluation frameworks and structured approaches to model quality measurement.
• Awareness of inference optimization strategies balancing latency, cost, and accuracy in LLM implementations.
• Proven track record deploying and managing machine learning APIs in cloud environments such as AWS or GCP.

What Numerator Provides

• An inclusive and collaborative company culture with an open environment that adapts dynamically to changing needs.
• Competitive market-level total compensation.
• Volunteer time off alongside charitable donation matching programs.
• Robust support for career advancement, including mentorship, leadership training, conference access, and employee resource groups.