11 days ago
Remote, WorldwideMid Level
Responsibilities
- Own customer engagements end to end, from feasibility and technical planning through implementation, delivery, validation, and post-delivery support.
- Translate customer model-development goals into executable technical plans with clear acceptance criteria.
- Build and operate systems that process, analyze, measure, quality-check, curate, and deliver large-scale video datasets.
- Develop tooling to characterize unknown datasets and respond to high-volume and bespoke data requests.
- Partner with product, platform engineering, Data Lab, commercial stakeholders, and the vertical GM to turn customer learnings into reusable platform capabilities.
- Define video FDE playbooks, reusable tooling, quality standards, and a roadmap for increasing delivery capacity.
- Manage multiple concurrent customer deals, partner datasets, customer requests, and architecture decisions.
Requirements
- 3+ years of experience as an engineer, including meaningful exposure to customers or external technical stakeholders.
- Direct experience working with media data, preferably video.
- Experience building and operating systems that process, analyze, or deliver data at scale.
- Ability to translate ambiguous requirements, communicate trade-offs, and build trust with technical stakeholders.
- Demonstrated end-to-end ownership from problem definition through implementation, validation, and support.
- Comfort working with ambiguity, multiple concurrent priorities, time-sensitive customer work, and availability outside standard hours when deals are live.
- Preferred: hands-on video processing at scale, including codecs, transcoding, ffmpeg, shot detection, frame sampling strategies, or perceptual quality measurement.
- Preferred: startup or early-stage company experience, Python and SQL experience, search or semantic retrieval experience, or experience with vector embeddings and ML-assisted data curation.
- Preferred: experience evaluating or deploying vision-language models and building evaluation harnesses.
- Preferred: product engineering experience or a strong product mindset, and experience with AWS, Databricks, Dagster, or Vercel.
