Salary Competitive DOE
Boston, 2 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 productionizing large language models, integrating them with backend systems, and optimizing their performance. You’ll collaborate closely with data science, DevOps, and leadership to shape the AI infrastructure that drives the company’s automation solutions.
What You’ll Do:
- Build, fine-tune, and productionize 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, DevOps, and leadership to deliver scalable machine learning solutions.
What We’re Looking For:
Essential Skills & Experience:
- Strong proficiency in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Hands-on experience fine-tuning LLMs (Hugging Face, PEFT).
- Familiarity with AWS (especially S3 for model management) 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.
- Experience with RLHF or preference optimization.
- 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.
If you’d like to hear more, please apply or get in touch!
