Senior Machine Learning Engineer

Talantir; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Talantir is seeking a Senior Machine Learning Engineer in San Francisco, with hybrid work flexibility. You will join an early-stage AI company building a platform to transform materials discovery and sourcing, handling data at scale and enabling intelligent search, ranking, and recommendations across millions of products.

You will work on core ML systems, deploy models, and shape platform evolution as it scales, collaborating with product and engineering to move ideas from conception to launch.

Qualifications

• Experience building and deploying ML systems in production.
• Strong Python and ML library experience.
• Hands-on with LLMs, RAG, or NLP workflows.

Responsibilities

• 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.

Skills

Python

ML libraries

LLMs

NLP

Unstructured data

Job description

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