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, 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 useful 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.
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 what should be prioritized next.
Protocol design: Design scientific data collection workflows that produce consistent, auditable, model-useful outputs.
Expert network buildout: Recruit, evaluate, and manage specialized domain experts and technical contributors.
Measurement rigor: Define what good data means for each program: experimental setup, metadata, controls, sampling plans, review rubrics, and failure modes.
Quantitative analysis: Use data, statistics, and operational metrics to identify bottlenecks, quality drift, and opportunities to improve collection throughput.
Dataset delivery: Own the path from first pilot to production dataset, including staffing, timelines, QA, escalation, customer review, and final delivery.
Quality systems: Build quality checks that catch scientific, procedural, and annotation errors before data reaches the customer.
Cross-functional execution: Partner with data operations, engineering, product, legal, finance, and recruiting to remove blockers and keep programs moving.
Operating cadence: Run the weekly operating rhythm: dashboards, customer updates, expert performance reviews, issue logs, and postmortems.
Repeatable playbooks: Turn successful pilots into repeatable scientific data collection playbooks that can scale across customers and domains.
Vendor and lab coordination: Manage external labs, contractors, equipment constraints, sample logistics, compliance considerations, and documentation requirements where needed.
Domain expansion: Identify adjacent scientific data opportunities and help Mecka build the operating muscle to serve them.
Internal standards: Raise the bar for how Mecka scopes, collects, reviews, and ships scientific datasets.
Field experience leading or working with scientific teams: 2+ years running or supporting labs, field studies, or research operations — leading or working alongside lab technicians, research assistants, study coordinators, or scientific contributors. You speak the lingo, set the standard, and earn the respect of the scientists you work with — but your craft is operations, not research.
Domain fluency: You have worked inside science long enough to know how a lab actually runs — protocols, sample handling, calibration, quality control, reviewer disagreements, and deadline pressure on principal investigators. You do not need to do the science to lead the people doing it.
Quantitative ability: Comfortable with experimental design, statistical reasoning, operational metrics, and data-driven decision-making.
Project ownership: Track record owning complex, cross-functional programs with external stakeholders and hard delivery dates.
Customer-facing judgment: You can build trust with technical customers, clarify ambiguous asks, and communicate risk without hiding the hard parts.
Experience running scientific data collection, benchmarking, research operations, or research programs with many contributors.
Experience hiring, leading, or working with lab technicians, research assistants, or scientific contributors in an operations setting (biotech, academic lab, pharma, CRO, autonomy/AI data ops, clinical research).
Comfortable in research environments — labs, field studies, expert interviews — not just managing from a deck.
Working knowledge of experimental design fundamentals: control conditions, blinding, inter-rater agreement, statistical significance.
Familiar with research operations tooling: LIMS, electronic lab notebooks, annotation/eval platforms (Label Studio, Scale, Surge), or research collaboration tools (Notion, Confluence, Quarto).
Comfortable reading papers on arXiv or Google Scholar, summarizing methods, and evaluating claims.
Background at an AI lab, data company, technical software company, or research-heavy startup.
Familiarity with data annotation, evaluation datasets, expert-in-the-loop workflows, or model training data operations.
Ability to recruit and assess scientific experts quickly, including knowing what good work looks like in a given domain.
Fluent in spreadsheets and modern AI tools to analyze experimental and program data — sample sizes, variance, reviewer agreement, throughput.
Builder mentality: you write the protocol the first time, tighten it after the pilot, and turn it into a system by the third run.
Direct, low-ceremony, and precise with customers and internal teams.
High-agency; you do not wait for perfect process before moving a program forward.
Detail-oriented without becoming academic — the goal is useful, trusted data shipped on time.
Calm under pressure when an experiment fails, an expert drops, or the customer changes scope.
Motivated by the chance to define how scientific data gets produced for embodied AI and frontier models.
You will own scientific data programs that directly shape how leading AI labs evaluate and train models.
You will build a new operating category at Mecka: high-trust, domain-specific data acquisition for scientific work.
You will work across research, operations, and customer teams instead of sitting in a narrow project-management lane.
You will turn ambiguous scientific requirements into datasets that customers can actually use.
You will help define the standards Mecka uses as it expands into more technical data domains.
Within 12 months, you have delivered multiple scientific data acquisition programs from scoping through final customer acceptance.
Customers trust you as the owner of scientific program quality, timeline, and tradeoff decisions.
Mecka has a repeatable playbook for recruiting experts, designing protocols, collecting data, reviewing quality, and shipping scientific datasets.
Quality issues are caught early through clear rubrics, controls, dashboards, and review processes you built.
Your programs have expanded from pilots into larger production work because the first deliveries were trusted and useful.
Compensation Range: $150K - $200K
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