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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
You will serve as a Forward Deployed Data Delivery Engineer, operating on the frontier with customers to transform messy, real-world capture data—much of it raw video—into beautiful, reliable, model-ready datasets. This is a senior, high-trust role with significant autonomy where you'll combine data engineering, hands-on analysis, and product judgment to deliver datasets customers can train and ship on, while making delivery systems more reliable with each engagement.
Key responsibilities include:
**Customer Delivery & Technical Ownership**
- Own end-to-end delivery of customer datasets: requirements gathering, validation, iteration, and final handoff
- Serve as the technical point of contact, communicating clearly, setting expectations, and closing loops
- Convert one-off customer needs into durable internal improvements—tooling, pipelines, and standards that accelerate future deliveries
**Data Systems & Pipelines**
- Build, debug, and harden data pipelines across ingestion, transformation, QA, and export
- Work fluently across storage and database paradigms (SQL, NoSQL, object storage), selecting the right tool for each job
- Establish reliable dataset "contracts": schemas, versioning, provenance, and reproducible builds
**Dataset Quality & Signal**
- Define and measure dataset quality for specific tasks: coverage, diversity, balance, label fidelity, and fitness for customer models
- Build quality scorecards and coverage/diversity reports that make dataset health legible to customers and internal teams
- Query and slice large corpora to maximize customer fit—surface exactly the data matching target distributions
- When signal is missing or weak in raw video, diagnose the issue and partner with perception/ML pipeline teams to extract or improve it upstream
**Requirements**
- Significant hands-on experience designing, building, debugging, and operating production software systems; comfort going deep with senior engineers on architecture, APIs, data flows, failure modes, reliability, and technical tradeoffs
- Proven experience personally owning substantial technical projects from ambiguous problem through architecture, implementation, deployment, and production support
- Direct experience working with external customers or partners to understand technical requirements, translate them into solutions, and remain accountable through successful delivery (internal ticket-based support alone is insufficient)
- High agency and strong generalist bent—comfortable moving across data, delivery process, and whatever the problem needs; taking on problems beyond defined scope without waiting to be asked
**Nice to Have**
- Working literacy in video understanding, embeddings, and encoders—enough to reason about what a dataset teaches a model and where signal is missing
- Experience building data-quality, coverage, or diversity tooling
- Background adjacent to ML, computer vision, or robotics data
