Machine Learning Engineer; LLM

Machine Learning Engineer (LLM)

Compensation: $170,000 - $200,000+ (DOE)
Location: Boston or Berkeley, flexible 2-3 days per week in office

We’re working a fast‑growing AI company on a mission to automate complex workflows in the financial services sector, starting with insurance. Their technology leverages cutting‑edge AI to simplify high‑value processes, from multi‑turn conversations to full workflow automation.

As an ML Engineer within LLMs, you’ll be building and scaling advanced AI systems that power intelligent, multi‑agent workflows. You’ll take ownership of designing, fine‑tuning, and product ionizing large language models, integrating them with backend systems, and optimizing their performance. You’ll collaborate closely with data science, Dev Ops, and leadership to shape the AI infrastructure that drives the company’s automation solutions.

• Build, fine‑tune, and product ionize large language model (LLM) pipelines, including PEFT, RLHF, and DPO workflows.
• Develop APIs, data pipelines, and orchestration systems for multi‑agent, multi‑turn AI conversations.
• Integrate models with backend services, including voice orchestration platforms and transcript generation.
• Optimize model usage and efficiency, transitioning from external APIs to in‑house solutions.
• Collaborate cross‑functionally with data scientists, Dev Ops, and leadership to deliver scalable machine learning solutions.

• Strong proficiency in Python and ML frameworks (e.g., scikit‑learn, Tensor Flow, PyTorch).
• Hands‑on experience fine‑tuning and training LLMs.
• Experience with PEFT, DPO, Prefence Optimization, post‑training, supervised fine tuning, RLHF.
• Familiarity with AWS suite and deploying ML models to production.
• Ability to reason deeply about ML principles, architectures, and design choices.
• Knowledge of multi‑agent orchestration and conversational AI systems.

• Background in voice AI, speech‑to‑text, or text‑to‑speech systems.
• Exposure to financial services or insurance applications.
• Familiarity with optimizing models for long‑context scenarios.

For additional information or to apply, please get in touch or apply directly.

Not Applicable

Full‑time

Information Technology