Forward Deployed Data Delivery Engineer

About the role

Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate 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. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware. We are hiring a Forward Deployed Data Delivery Engineer to operate on the frontier with customers: take messy, real-world capture data - much of it raw video - and turn it into beautiful, reliable, model-ready datasets, while owning the technical relationship end-to-end. This is a senior, high-trust role with significant autonomy. You'll combine data engineering, hands-on analysis, and product judgment to deliver datasets customers can train and ship on - and to make our delivery systems more reliable every time you do. Own the customer-facing delivery loop for world-class robotics datasets. High autonomy, high trust, and direct impact on customer success and revenue. Work across the full stack of the problem: data, pipelines, analysis, and delivery quality. Sit at the exact point where raw, messy, real-world data becomes the thing that makes embodied-AI models work.

Responsibilities

• Customer Delivery & Technical Ownership: Own the end-to-end delivery of customer datasets: requirements, validation, iteration, final handoff. Be the technical point of contact: communicate clearly, set expectations, and close loops. Turn one-off customer needs into durable internal improvements - tooling, pipelines, and standards that make every future delivery faster and safer.
• 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) and pick the right tool for the job. Establish reliable dataset "contracts": schemas, versioning, provenance, and reproducible builds - so every dataset has a clear source of truth.
• Dataset Quality & Signal: Define and measure what makes a dataset good for a given task: coverage, diversity, balance, label fidelity, and fitness for the customer's model. 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 that matches a target distribution, not just bulk volume. When the signal a customer needs is missing or weak in the raw video, diagnose it and partner with the perception/ML pipeline teams to extract or improve it upstream.

Requirements

• Significant hands-on experience designing, building, debugging, and operating production software systems. Comfortable going deep with senior engineers on architecture, APIs, data flows, failure modes, reliability, and technical tradeoffs—not just integrating or demonstrating existing tools.
• Proven experience personally owning substantial technical projects from an 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 not sufficient.
• High agency and a strong generalist bent — comfortable moving across data, delivery process, and whatever else the problem needs, taking on problems beyond your defined scope without waiting