Senior Machine Learning Engineer | Hybrid | San Francisco
Berkley Hunt is partnering with a well-funded, early-stage AI company building a platform that is transforming how materials are discovered and sourced across the construction industry.
This is not a typical AI use case. The team is tackling a highly manual, fragmented space where workflows are still driven by PDFs, spreadsheets, and disconnected supplier data. They are building systems that can structure and understand this data at scale, enabling intelligent search, ranking, and recommendations across millions of products. You will be joining early, working on core machine learning systems, and helping shape how the platform evolves as it scales.
What You’ll Do
- Build and deploy machine learning models into production environments
- Work on search, ranking, and recommendation systems to improve relevance and accuracy
- Develop and optimise NLP and document extraction pipelines across unstructured data
- Design and implement retrieval-based systems using LLMs and embeddings
- Analyse data and continuously improve model performance in real-world settings
- Collaborate closely with product and engineering to take features from idea through to launch
- Own problems end-to-end, from experimentation to production impact
What We’re Looking For
- Experience building and deploying ML systems in production
- Strong experience with Python and common ML libraries
- Hands-on experience with LLMs, RAG systems, or NLP workflows
- Experience working with unstructured data and extraction pipelines
- Understanding of search, retrieval, or recommendation systems
- Ability to operate in a fast-paced, early-stage environment with high ownership
- Strong communication and problem-solving skills
Bonus Skills
- Experience with vector databases and embedding-based retrieval
- Experience fine-tuning LLMs for domain-specific tasks
- Experience improving search relevance or recommendation systems
- Background in data-intensive or product-focused environment
