Machine Learning Engineer (NLP)

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.

We are seeking a Machine Learning Engineer who is passionate about applying cutting-edge machine learning to solve critical challenges in data privacy and security. In this role, you will be responsible for developing scalable natural language processing (NLP) solutions to extract insights and enforce data governance policies across massive and complex datasets.

You will work closely with cross-functional partners—including software engineers, product leaders, and senior stakeholders—to deliver robust, production-grade ML systems. This position offers the opportunity to shape the ML infrastructure and modeling strategy at a fast-growing company that places a high premium on innovation, velocity, and impact.

• Design and implement NLP models focused on tasks such as Named Entity Recognition (NER), Text Classification, Document Summarization, Clustering, and Structured Information Extraction.

• Fine-tune state-of-the-art transformer-based models and adapt them to Teleskope’s domain-specific needs.

• Build scalable pipelines for training, evaluation, and deployment of machine learning models in production environments.

• Optimize model performance and inference speed through techniques such as model compression, GPU acceleration, and distillation.

• Define requirements and implement processes for quality assurance and model validation throughout the ML lifecycle.

• Contribute to the design of our ML architecture and infrastructure, ensuring reliability, maintainability, and performance across diverse deployment environments.

• Collaborate closely with leadership to align ML initiatives with core product objectives and platform scalability.

• Minimum of 6 years of experience in machine learning engineering, with a strong focus on NLP applications.

• Demonstrated experience building and deploying machine learning systems in production environments at scale.

• SQL experience and the use of regular expressions for data extraction and manipulation.

• Experience curating and preprocessing large-scale training datasets for model development.

• Strong communication skills with the ability to clearly articulate technical concepts and decisions to diverse stakeholders.

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Mathematics, Statistics, or a related technical discipline—or equivalent professional experience.

• Must be based in New York City - this is an in office role.

Nice to Haves

• Familiarity with GPU-accelerated inference and model optimization tools such as NVIDIA Triton Server and ONNX.

• Proven ability to define and implement quality control and validation mechanisms in ML pipelines.

• Proficiency in Python and familiarity with core ML/NLP frameworks such as PyTorch and HuggingFace Transformers.

• Exposure to compiled programming languages, particularly Go.

• Experience building NLP systems that support multilingual data or operate across multiple languages.

• Background in deploying optimized models in diverse compute environments (e.g., cloud-native, on-premises).

What You'll Get

• An opportunity to join an early stage startup in one of today’s fastest growing markets

• Space to support industry-leading customers and build impactful solutions that drive their data security programs

• Room to learn, grow, and rapidly advance in your career in machine learning

• A beautiful, well-stocked office in NYC’s Financial District

• Flexible vacation and work from home days

• Competitive salary and meaningful equity

• Health, vision, dental, 401k and more benefits, heavily subsidized by Teleskope

At Teleskope, we value team members who combine technical excellence with pragmatic execution. Ideal candidates demonstrate not only a deep understanding of machine learning systems but also a strong sense of ownership and a drive to deliver impactful solutions with urgency and precision.