Back to search
Harnham Linkedin · Posted 17d ago

Machine Learning Engineer, LLM

New York City, New York, United States

Linkedin
Continue to application Add your email once, then Caio opens the original posting.

Indexed description

Machine Learning Engineer, LLM (Applied AI/NLP)

Location: New York (Hybrid)

Compensation: $180K-$220K base + equity + bonus

About the Company

Join a fast-growing AI startup transforming how development teams build and scale modern applications. Their intelligent platform uses LLMs to automate coding tasks, streamline workflows, and boost developer productivity.

The Role

Lead the design and deployment of advanced NLP/LLM solutions that directly impact engineering workflows code generation, documentation, and review automation. Collaborate with senior leadership to take models from research to production.

Key Responsibilities

  • Build and fine-tune LLMs for developer-focused use cases
  • Optimize models using prompt engineering, RAG, and inference techniques
  • Collaborate across teams to align AI capabilities with product goals
  • Mentor junior ML engineers and contribute to internal innovation

What You'll Bring

  • 2-8 years in applied NLP/LLM development
  • Strong Python and ML stack (PyTorch, HuggingFace, etc.)
  • Experience with large code/text datasets and transformer models
  • Advanced degree in CS, AI, or NLP (PhD preferred)
  • Bonus: Publications in top-tier ML/NLP conferences

Why Join?

  • Competitive pay + equity + bonus
  • Direct impact on product innovation and IP
  • Full benefits: health, vision, dental, 401(k), generous PTO

How to Apply

To explore this opportunity, please submit your resume to Luc Simpson-Kent using the Apply link on this page.

Desired Skills and Experience

Python, Machine Learning Frameworks, NLP Expertise, LLM Development, Data Handling

Free. 20 seconds. No password. See every match in this search.

Create a free Caio profile to unlock the full index and keep your job-search signal for future recommendations.

Unlock free search