Company Background:
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.
Role:
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 tooling.
Responsibilities:
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
Define and enforce quality control standards across all labeled data, implementing automated checks and audit workflows
Partner with researchers to translate perception model needs into data collection strategies, identifying gaps in coverage across object types, scenes, lighting conditions, and sensor modalities
Build dashboards and metrics to monitor dataset diversity, class balance, and domain coverage
Close the loop on the data flywheel: track how labeled data flows into training, surface failure modes, and drive iteration on the pipeline from collection through to model improvement
Evaluate and integrate new data sources
Define labeling taxonomies and annotation specifications
Qualifications:
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
Bonus: experience with computer vision data, annotation platforms, or labeling operations