Machine Learning Engineer

The Machine Learning Engineer role at Teleskope in New York, NY (hybrid) focuses on strengthening element and entity classification pipelines across data sources, bridging research and production, with an in-office requirement in NYC.

Responsibilities

• Lead a team responsible for building, maintaining, and scaling production ML pipelines for entity extraction and data element classification across diverse data types.
• Collaborate with analytics and ML oriented data scientists to convert experimental results into deployed systems and remove technical bottlenecks.
• Develop evaluation, monitoring, and regression testing frameworks in partnership with quality control.
• Pursue incremental improvements to classification models and pipelines with a focus on measurable impact.
• Champion best practices around model deployment, versioning, and operational monitoring.

Requirements

• 4+ years of experience building and deploying ML systems in production.
• Strong experience with NLP systems and supervised entity classification models.
• Comfort moving from research prototypes to CI/CD driven production pipelines.
• Proficiency in Python with common ML tooling.
• Strong analytical instincts and problem solving under ambiguity.
• Collaborative communicator who can partner across data science and engineering functions.

Technologies

• Python

Benefits

• A high impact role at an early stage startup in a fast growing market.
• Ownership over ML systems that directly power every customer workflow on the platform.
• Opportunity to contribute to core ML systems that power our classification service.
• A beautiful, well stocked office in NYC's Financial District.
• Flexible vacation and work from home days.
• Salary: USD 160,000 - 210,000 per year.
• Competitive salary and meaningful equity.
• Health, vision, dental, 401k and more benefits, heavily subsidized by Teleskope.

What we value

At Teleskope, we prize engineers who build durable systems, combine deep ML expertise with pragmatic execution, own critical infrastructure, and ship reliable solutions that advance real world security and privacy outcomes.