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.
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.
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.
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.
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.
Field experience leading or working with hardware teams: 2+ years running or supporting hardware programs, manufacturing operations, or field deployments — leading or working alongside hardware engineers, manufacturing engineers, technicians, suppliers, and field operators. You speak the lingo, set the standard, and earn the respect of the engineers you work with — but your craft is operations, not engineering.
Domain fluency: You have worked inside hardware long enough to know how a product actually gets built — design reviews, BOMs, tolerance stack-ups, EVT/DVT/PVT, supplier dependencies, yield issues, and deadline pressure on engineering teams. You do not need to engineer the hardware to lead the people who do.
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.
Experience in robotics, industrial automation, electronics, automotive, aerospace, or hardware startups.
Experience hiring, leading, or working with hardware engineers, manufacturing engineers, quality engineers, technicians, or supplier-side contributors in an operations setting (robotics startup, contract manufacturer, OEM, supply chain, field deployment, hardware program management).
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.
Good to have — basic comfort with CAD (SolidWorks, Onshape, Fusion 360) and electrical schematics so you can follow a hardware review meeting without getting lost.
Good to have — 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.
Direct, practical, and comfortable working with both engineers and operators.
High-agency; you do not need a complete playbook to start building one.
Detail-oriented about quality, but focused on shipping useful data rather than creating process for its own sake.
Calm when a supplier slips, an expert underperforms, or a customer changes requirements midstream.
Motivated by the chance to make physical-world engineering knowledge usable for frontier AI systems.
You will own hardware and manufacturing data programs that help AI labs understand real physical systems.
You will build the operating bridge between robotics companies, manufacturing experts, and model-training teams.
You will work on problems where quality depends on domain judgment, not generic annotation throughput.
You will help Mecka define how technical data from the physical world should be captured and trusted.
You will turn successful pilots into scalable programs across robotics, hardware, and industrial domains.
Within 12 months, you have delivered multiple hardware or manufacturing data programs from customer scoping through final acceptance.
Customers trust you as the owner of program quality, timeline, and domain tradeoffs.
Mecka has a repeatable playbook for recruiting hardware experts, designing collection workflows, reviewing outputs, and shipping trusted datasets.
Quality and throughput improve over time because you built the metrics, review loops, and escalation paths that make them visible.
Your programs have expanded from pilots into larger production work because the data was accurate, useful, and delivered on time.
Compensation Range: CA$150K - CA$200K
