Forward Deployed Engineer (FDE)

About the role

We're hiring our founding Forward Deployed Engineers. You'll embed with our most strategic customers and own their success end-to-end, from translating their model and data needs into concrete collection / annotation / delivery specs, to making sure the data we ship actually moves their model performance.

This is a critical role that today, our founders and core engineers absorb directly. As an FDE, you'll be the single technical owner for a portfolio of accounts, sitting at the intersection of customer delivery and core platform development, and you'll help define our forward-deployed motion as we grow.

What you'll do

• Own end-to-end delivery for a set of strategic customers: scope requirements, sequence delivery, QA before shipping, and clear blockers.
• Be the first technical responder for your accounts: triage questions, manage expectations on timelines and on still-maturing data products, and escalate only what genuinely needs it.
• Represent XDOF's technical depth to sophisticated customers while protecting proprietary methodology.
• Close the loop: turn recurring customer needs and delivery pain into concrete input for our data-engine, pipeline, and annotation roadmaps, and codify what works into reusable playbooks.
• Grow the motion: as the role matures, lead platform-integration engagements and model fine-tuning / evaluation services for enterprise customers.

What we're looking for

• 5+ years of engineering experience, ideally including customer-facing or forward-deployed work.
• Founding-engineer profile, strongly preferred: you've owned many things at once and solved hard problems without a playbook, and you want to talk to customers.
• Strong production coding (Python preferred), with real experience shipping and operating data pipelines or infrastructure.
• High agency and comfort with ambiguity: you drive customer outcomes rather than wait for a spec.
• Excellent communication and low ego: you can run discovery, push back respectfully on unrealistic asks, and explain hard tradeoffs to both engineers and executives.

Nice to have

• Background in robotics, embodied AI, autonomy, or ML data infrastructure.
• Familiarity with robot-learning data (teleoperation, egocentric, VLA / robot foundation models) and formats like MCAP, HDF5, etc.
• Forward-deployed or solutions experience at an enterprise or frontier-AI company.