Strategic Project Lead, Hardware – Toronto

Mecka•Markham, ON

2d•Onsite

About The Position

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. We're hiring a Strategic Project Lead, Hardware to own hardware and manufacturing data acquisition programs end-to-end for AI-lab and robotics customers. You will scope customer needs, recruit and manage hardware experts, design data collection workflows, own quality, and ship datasets that reflect how real physical systems are designed, built, tested, and improved. This is a senior individual contributor role for someone who knows manufacturing and hardware engineering well enough to separate useful signal from noise. You will sit between customer teams, expert contributors, and Mecka's internal data operations team, with clear accountability for delivery.

Requirements

Hardware or manufacturing depth: 2+ years in manufacturing, hardware engineering, robotics operations, industrial engineering, quality, technical program management, or a related field.
Market fluency: Deep working knowledge of how hardware products are designed, manufactured, tested, scaled, and supported.
Quantitative ability: Comfortable with production metrics, yield, defect analysis, quality systems, operational dashboards, and data-driven decision-making.
Project ownership: Track record owning complex cross-functional programs with customers, vendors, technical contributors, and hard deadlines.
Customer-facing judgment: You can build trust with technical customers and explain tradeoffs clearly without overpromising.

Nice To Haves

Experience in robotics, industrial automation, electronics, automotive, aerospace, or hardware startups.
Comfortable in factory and field environments — you have been on a production line, not just read about one.
Familiar with hardware lifecycle tooling: BOM systems, ERP/MRP basics, PLM, or Jira/Linear for engineering programs.
Able to follow a hardware review meeting — basic comfort with CAD (SolidWorks, Onshape, Fusion 360) and electrical schematics.
Familiarity with quality systems (DFMEA, PFMEA, 8D, statistical process control) or willingness to learn quickly.
Background working with suppliers, vendors, quality teams, or field operations.
Familiarity with data annotation, evaluation datasets, expert-in-the-loop workflows, model training data, or technical benchmarking.
Ability to recruit and assess hardware experts quickly because you know what strong engineering and manufacturing judgment looks like.
Fluent in spreadsheets and modern AI tools to analyze production data — defect rates, yield, throughput, capacity.
Builder mentality: you write the runbook, pressure-test it in the field, revise it with the team, and turn it into a repeatable system.

Responsibilities

Customer Engagement: Hardware scoping: Work with AI labs, robotics companies, and technical customers to translate hardware and manufacturing needs into executable data acquisition programs.
Account ownership: Own the customer relationship for your programs — requirements, timelines, risks, deliverables, and quality expectations.
Technical translation: Convert broad customer goals into clear data specs, expert profiles, collection workflows, review rubrics, and acceptance criteria.
Tradeoff management: Communicate what is feasible, what will require deeper expertise, where quality risk exists, and how scope should evolve.
Data Collection Methodology: Workflow design: Design data collection methods across hardware engineering, testing, and related technical operations.
Expert network buildout: Recruit, evaluate, and manage hardware engineers, technicians, quality engineers, and other technical contributors.
Process rigor: Define how data should be captured so it is consistent, auditable, and useful for model training and evaluation.
Quantitative analysis: Use throughput, defect, yield, review, and quality metrics to improve collection methods and identify weak points in the program.
Quality & Execution: Dataset delivery: Own the delivery path from pilot through production dataset, including staffing, schedules, QA, customer review, and final shipment.
Quality systems: Build review loops that catch incorrect reasoning, missing context, low-quality demonstrations, process errors, and domain-inaccurate outputs.
Cross-functional execution: Partner with operations, recruiting, engineering, product, legal, and finance to get the right people, tools, and processes in place.
Operating cadence: Run the program rhythm: dashboards, customer updates, expert calibration, issue tracking, and postmortems.
Program Scaling: Repeatable playbooks: Turn successful hardware data pilots into repeatable operating playbooks across manufacturing and robotics domains.
Supplier and site coordination: Manage vendors, external experts, facility constraints, equipment access, documentation, and confidentiality requirements where needed.
Domain expansion: Identify adjacent hardware data opportunities across robotics, electronics, mechanical systems, and related technical domains.
Internal standards: Raise Mecka's bar for how hardware and manufacturing datasets are scoped, collected, reviewed, and delivered.