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Tech Lead Manager, AI / Machine Learning

Numerator

Toronto, Ontario, CanadaJobNo compensation foundTracked 3w agoSeen in employer's feed 2 days ago

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At a glance

Compensation
No compensation found
Location
Toronto, Ontario, Canada
Work Authorization
Not specified

Job overview

Numerator is hiring a Tech Lead Manager, AI / Machine Learning. Numerator is seeking a hands-on Tech Lead Manager for its Machine Learning team. This 50/50 player-coach role involves managing a small team of ML engineers while contributing to code, system design, and GenAI feature delivery. The manager will shape the technical roadmap and foster team growth within an established platform handling massive data volumes and requests.

Key focus areas include Manage and grow a small team of AI software engineers, Stay deeply technical by contributing to the codebase, and Design GenAI systems and own meaningful slices of delivery.

Successful candidates bring 2+ Years Engineering Or Data Science Management Experience, 4+ Direct Reports, and Performance Conversations. Important skills include Python, Software Engineering Fundamentals, Clean Code, Testable Code, REST API Design, and Debugging. Preferred (not required): Managing Engineers Working Across Traditional ML And GenAI, Agentic Orchestration Frameworks, Fine-Tuning With Modern Techniques, and Domain Adaptation For NLP Tasks.

Skills & qualifications

RequiredNice to have

Skills

PythonSoftware Engineering FundamentalsClean CodeTestable CodeREST API DesignDebuggingCI/CDLLM APIsContext EngineeringRAGTool/Function CallingAgentsEvaluation MethodologyTechnical JudgmentData AcumenProduct OrientationPeople-First Management StyleSelf-ImprovementManaging Engineers Working Across Traditional ML and GenAIAgentic Orchestration FrameworksFine-Tuning With Modern TechniquesDomain Adaptation for NLP TasksPyTorchHugging FaceLLM Evaluation FrameworksStructured Approach to Measuring Model QualityInference Optimization AwarenessBuilding and Deploying Robust Machine Learning APIs in Cloud EnvironmentsAWSGCP

Qualifications

2+ Years Engineering or Data Science Management Experience4+ Direct Reports4+ Years Hands-on ML or GenAI Engineering ExperienceProduction Systems Experience

Full job description

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.

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.

You've read the whole posting — now see how you match it.