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Machine Learning Engineer – LLM Engineering at Liberate
San Francisco, California, United States -
Full Time

Start Date

Immediate

Expiry Date

27 Jan, 26

Salary

0.0

Posted On

29 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, NLP, LLMs, Python, SQL, AWS, Conversational AI, Speech-to-Text, Text-to-Speech, Fine-Tuning, Supervised Fine Tuning, Preference Optimization, PyTorch, TensorFlow, AI Frameworks, Hugging Face

Industry

technology;Information and Internet

Description
About Us Liberate is reimagining how the $2.7T insurance industry works. By starting with voice, the most valuable and complex channel in insurance, the company proved that even the hardest problems can be automated. Now expanding into full workflow automation across sales, servicing, and claims, Liberate is building toward a bold vision: reasoning agents capable of managing the entire spectrum of carrier and broker operations. Trusted by leading brokers and carriers and powered by a team with experience at Metromile, Google, Stripe, and other category-defining companies, Liberate is shaping the future of insurance. About the role We’re looking for a Machine Learning Engineer to help us push the boundaries of LLM-powered conversational AI and automation pipelines for insurance related tasks. Location: Boston, MA or San Francisco, CA hybrid role, 2 days per week in-office What You’ll Do Build LLM-driven pipelines for information extraction and workflow automation in insurance. Develop multi-turn, long-context agents that work seamlessly across modalities (voice, text, email). Fine-tune and deploy models to deliver accurate, compliant, and high-impact solutions. Work with cutting-edge speech, NLP, and multimodal AI technologies. What We’re Looking For Strong ML/NLP background with hands-on experience in LLMs. Experience in working with Python, SQL Experience of developing and deploying services and application using AWS Experience with conversational AI, speech-to-text, or text-to-speech systems. Post training methods for LLMs such as supervised fine tuning and preference optimization Nice to have: PyTorch/TensorFlow Working knowledge of AI frameworks (Hugging Face, LangChain, etc.).
Responsibilities
Build LLM-driven pipelines for information extraction and workflow automation in insurance. Develop multi-turn, long-context agents that work seamlessly across modalities.