The posting, in Teleskope's own words
archived Oct 3, 2026About Teleskope Teleskope is redefining data security for the AI era with the only dedicated platform that combines precise visibility with automated remediation. Teleskope continuously scans, catalogs and classifies data in-motion and at-rest while automating policy-based actions, helping organizations proactively manage data sprawl while securely enabling AI adoption. Following our $25 million Series A round, Teleskope is entering a high-growth phase backed by top-tier investors and exceptional product-market fit.
About the role
We're hiring a Tech Lead to own the technical direction of our element classification system, the engine that powers every customer workflow on the platform. You'll lead the engineers and data scientists building entity classification across text, documents, relational data, and OCR. You'll set the roadmap, decide where the pipeline needs to improve, and make sure our methods stay at the state of the art. This is a hands-on leadership role. You'll split your time between setting direction for the team and doing the hardest technical work yourself. You'll report to the Director of Data Science and ML and work closely with annotation, QC, engineering, and product. This is a hybrid role requiring 3+ days in-office in New York City .
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What You'll Do :
Own the element classification roadmap: set priorities, sequence the work, and align the team and stakeholders on tradeoffs. Lead the team's technical work: break large problems into scoped projects, assign and unblock work, review designs and code, and hold a high bar for quality. Own evaluation strategy: build benchmarks, regression suites, and production monitoring that show where the pipeline is failing and why, so the team focuses on the gaps that matter. Track new work in NLP, LLMs, and information extraction, and decide what to adopt. Run focused experiments and take the winners to production. Set the standard for data quality: refine labeling guidelines, work with annotators and qc teams on label quality, and build training and eval sets that reflect real world problems. Run delivery with checkpointed milestones, and keep leadership and cross-functional partners updated on progress, risks, and decisions. Set engineering practices for model deployment, versioning, CI/CD, and operational monitoring. Mentor and grow the team, and help with hiring as the team scales.