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Strategic Project Lead, Sciences

Job in Toronto, Ontario, C6A, Canada
Listing for: mecka
Full Time position
Listed on 2026-06-20
Job specializations:
  • IT/Tech
    AI Evaluation, Artificial Intelligence, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 80000 - 100000 CAD Yearly CAD 80000.00 100000.00 YEAR
Job Description & How to Apply Below

About Mecka AI

Mecka AI is building the data and deployment infrastructure for embodied intelligence. We collect, curate, and license the world's most useful robotics training data to leading AI labs, and we deploy real robotic systems with enterprise customers across hospitality, retail, QSR, pharmacy, logistics, and healthcare. We work with the foundation model teams shaping the next decade of robotics, and with the operators running real businesses today.

Quality, trust, and execution are core to our partnerships.

The Role

We're hiring a Strategic Project Lead, Sciences to own scientific data acquisition programs end-to-end for AI-lab customers. You will scope the work with the customer, recruit and manage scientific experts, design the data collection methodology, own quality, and ship datasets that are practical for frontier model training and evaluation.

This is a senior individual contributor role at the intersection of customer engagement, scientific operations, and data quality. You should be quantitative, hands‑on, and comfortable turning ambiguous research needs into operational programs that produce trustworthy data.

What You'll Own Customer Engagement
  • Scientific scoping:
    Work directly with AI labs and research teams to translate model needs into data acquisition programs across specialized technical and scientific domains.
  • Account ownership:
    Serve as the day‑to‑day owner for your customer program — timelines, risks, deliverables, quality, and trust all sit with you.
  • Technical translation:
    Convert open‑ended scientific requirements into clear protocols, acceptance criteria, and operating plans that internal teams and external experts can execute.
  • Customer narrative:
    Communicate tradeoffs clearly to customer stakeholders: what data is feasible, what will take longer, where quality risk exists, and …
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