Technical Program Manager
US$150,000 – US$200,000 / yr base salary
Posted 24 Sep 2026
Advertised as “Technical Program Manager, Robotic Data & Deliveries”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$150,000 – US$200,000 / yr |
|---|
About Mecka AI
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.
The Role
Own the program that turns Mecka’s ego-data pipeline into on-time, at-spec dataset deliveries to frontier AI labs. You will sit at the seam between our ego-data sales & delivery team and the engineering that produces the data — driving delivery execution against customer commitments while iteratively building the tooling, standards, and datasets that let us scale from bespoke drops to a repeatable delivery engine.
This is a hands-on, senior IC role. You will be equally comfortable running a delivery schedule with a demanding AI-lab customer, writing a script to reshape a dataset or query a pipeline, and defining the spec that makes the next delivery faster than the last.
What you will be doing:
• End-to-End Delivery Program Ownership: Act as the lead program owner for our highest-value ego-data deliveries. Drive cross-functional execution across Sales, Data Operations, Labeling, and Engineering to ship datasets that meet each customer’s spec, volume, and timeline — owning the master delivery schedule and holding contributors accountable to it.
• Delivery Tooling & Automation: Iterate on the tooling that packages, validates, and hands off ego datasets. Prototype and build — scripts, QA checks, packaging and manifest tooling, delivery dashboards — then partner with Engineering to harden what works into durable infrastructure. Every delivery should leave the pipeline faster and more automated than it found it.
• Dataset Quality & Standards: Define and enforce the standards that make an ego dataset "delivery-ready" — completeness, metadata, taxonomy, formatting, and QA gates. Turn recurring delivery pain into reusable specs, checklists, and automated validation so quality is built in, not inspected in.
• Sales & Customer Delivery Partnership: Partner with the ego-data sales & delivery team to translate customer requirements into concrete delivery plans, and translate pipeline realities back into commitments sales can confidently make. Serve as a senior point of contact on delivery scope, status, and escalations for key AI-lab accounts.
• Metrics & Reporting: Build data-driven reporting for the delivery function — time-to-delivery, dataset volume and yield, QA pass rates, rework, and SLA adherence. Use fleet- and pipeline-level data to spot bottlenecks and quality trends before they threaten a delivery.