Machine Learning Engineer

Berkley Hunt; New York (NY); Hybrid; USD 160,000 - 200,000; Full time

Job summary

Berkley Hunt is seeking a Machine Learning Engineer to own applied NLP systems in a startup environment. Responsibilities include developing NLP models for data extraction and insight generation, with a focus on independent project management. Ideal candidates will have strong Python skills and experience in rapidly changing environments.

Qualifications

• Strong Python skills; exposure to Go is a plus.
• Proficiency with Hugging Face, MLFlow, and GPU-accelerated environments.
• Hands-on experience with applied NLP tasks.

Responsibilities

• Own end-to-end development of NLP pipelines.
• Fine-tune transformer-based models using Hugging Face.
• Collaborate with engineering and product teams.

Skills

Python

NLP

Machine Learning

GPU acceleration

Experiment management

Education

Strong practical experience

Tools

Hugging Face

MLFlow

Job description

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This range is provided by Berkley Hunt. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$160,000.00/yr - $200,000.00/yr

Direct message the job poster from Berkley Hunt

Senior Recruiter @ Berkley Hunt | Veteran | Partnering with Venture Capital firms to build high-performing tech teams | Defense Tech

Machine Learning Engineer (NLP) | Hybrid – New York City | U.S. Work Authorization Required

Berkley Hunt is partnered with a seed-stage startup building intelligent systems to automate how companies secure and govern sensitive data. Founded by engineers with deep experience in cloud-scale infrastructure and privacy tooling, the company is replacing outdated, manual-heavy workflows with modern, ML-powered automation—helping security and data teams move faster without compromising control.

They’re backed by top-tier investors and are solving high-value problems with a builder-first culture. Now, they’re hiring a Machine Learning Engineer to take ownership of applied NLP systems, working across the full lifecycle from dataset gathering to model deployment.

About the Role

This role is ideal for someone who thrives in a startup environment and enjoys taking end-to-end ownership of ML solutions—from curating datasets to shipping models into production. You’ll focus on applied NLP tasks like clustering, classification, and structured data extraction, helping drive automation and insight from large, messy data sources.

You’ll be expected to work independently, manage experiments, and deploy production-grade models using tools like Hugging Face and MLFlow. You should be comfortable with GPU-accelerated workflows and have strong Python skills. A background in startups and experience working in high-autonomy environments is highly valued.

Responsibilities:

• Own end-to-end development of NLP pipelines, from dataset creation to deployment
• Build and deploy models for clustering, classification, and structured data extraction
• Fine-tune transformer-based models using frameworks like Hugging Face
• Manage experiments, pipeline versioning, and training lifecycle with MLFlow
• Optimize performance using GPU acceleration and model refinement techniques
• Collaborate with engineering and product teams to align ML initiatives with product goals
• Maintain and improve production ML systems over time

Requirements:

• Strong Python skills; exposure to Go is a plus
• Demonstrated experience owning ML projects from concept to deployment
• Proficiency with Hugging Face, MLFlow, and GPU-accelerated environments
• Hands-on experience with applied NLP tasks (e.g., clustering, classification), not just academic research
• Familiarity with managing and preparing large datasets independently
• Comfortable working in fast-paced, high-autonomy startup environments
• No formal degree required—strong practical experience is what counts
• Must be based in NYC and available for regular in-office collaboration

Nice to Have:

• Prior experience in seed or early-stage startups
• Experience refining or improving existing ML systems
• Familiarity with tools like ChatGPT or agentic AI (not required)
• Understanding of secure or regulated data environments (e.g., healthcare, finance, defense)

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Technology, Information and Media, IT System Data Services, and Data Infrastructure and Analytics

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