Joblaze summary

In the role of Data Operations Engineer at Specter, the individual is responsible for managing the entire data pipeline, from defining data requirements to ensuring high-quality labeling that supports research initiatives. Key skills include proficiency in Python and experience with data operations, particularly in machine learning contexts. This position is well-suited for someone with 1-3 years of relevant experience who is organized and capable of handling multiple projects simultaneously. Specter's small, fast-growing team emphasizes collaboration between engineering and research to enhance their AI-driven video sensor technology.

Joblaze insights

Listed today — first seen on Joblaze October 26, 2026. Last confirmed on Specter's careers page October 26, 2026.
Python appears in 69.2% of 78 comparable mid data engineering roles in United States; AI/ML appears in 7.7% of 78 comparable mid data engineering roles in United States.

Quick facts

How much experience is required?: 1–3 years of relevant experience for this Data Operations Engineer role.
What's the tech stack?: Joblaze extracted these technologies from the posting: AI/ML, Python.
What seniority level is this role?: Specter targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this Data Operations Engineer role at Specter.

From the original posting

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

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

Standard company text repeated across Specter's postings is omitted here.

Data Operations Engineer

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