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Data Operations Engineer

Specter · San Francisco · On-site

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About Specter

Search 55M companies, 550M people and every funding or M&A event in real time—surface hidden startups, size markets and act first. Backed by Entrepreneur First.

About the role

Own the end-to-end relationship with our data labeling provider, including task scoping, timeline management, and issue resolution Build and maintain internal tooling for labelers, including annotation interfaces, task pipelines, and dataset browsers

What they're looking for

  • 1-3+ years of experience in data operations, project management, or a technical coordination role, ideally supporting ML or engineering teams
  • Proficiency in Python and comfort building lightweight tools, scripts, and dashboards
  • Strong written and verbal communication skills, with experience managing external vendors or cross-functional stakeholders
  • Familiarity with ML workflows and how training data impacts model performance
  • Highly organized, with a track record of managing multiple concurrent workstreams
  • Self-directed and autonomous
More about this role

Specter's mission is to help automate the physical world.

Today, we build video sensors with state-of-the-art AI agents that answer any question, anywhere in their environments. Our systems can automatically detect and reason about any physical activity captured on camera, from security incidents (e.g. perimeter intrusion, theft, LPR), to safety monitoring (e.g. PPE detection, injured people), to operational efficiency (e.g. material tracking, congestion monitoring). We offer both long range wireless (1km range) and wired sensor variants to suit any deployment.

Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.

Specter is hiring a data operations engineer to build our research data operation. This individual will own the full pipeline from defining what data we need, to getting it labeled at high quality, to ensuring it meets the needs of our research team and ultimately improves our models. The role sits at the intersection of engineering and research, with a focus on building systems and...

Read the full posting on Specter's site ↗

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