Description
Kensho is seeking a mid-level Machine Learning Engineer to help develop and scale RAG systems across the company. This is a hands-on, full-lifecycle ML role with a strong emphasis on retrieval models, LLM orchestration, and system-level thinking.
The DRIVE Team at Kensho is focused on designing and deploying production-grade machine learning systems that power our next-generation agentic search pipelines. We specialize in building robust retrieval systems, scalable embedding infrastructure, and tightly integrated LLM pipelines that leverage unstructured data sources.
Responsibilities:
- Design and implement end-to-end RAG pipelines that integrate proprietary chunking algorithms, embedding models, vector databases, and data retrieval agents
- Build and optimize retrieval systems over large-scale proprietary datasets using advanced embedding techniques
- Develop LLM-based solutions that orchestrate retrieval, generation, and ranking to deliver high-quality, context-aware responses
- Investigate and solve challenges in vector search, chunking and indexing strategies, unstructured data retrieval evaluation, and GraphRAG
- Work closely with Product and Design teams to build ML-based solutions that enhance user experiences and meet business objectives
- Collaborate closely with the ML Operations team to create automated solutions for managing the entire ML systems lifecycle
Requirements:
- Bachelor's degree or higher in Computer Science, Engineering, or a related field
- 3+ years of significant, hands-on industry experience with machine learning, natural language processing (NLP), information retrieval systems and large-scale text processing
- Strong programming skills in Python, with a working knowledge of data processing tools and ML frameworks such as PyTorch, Transformers, and HuggingFace
- Experience working with machine learning libraries/frameworks for Large Language Model (LLM) orchestration
- Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation
- Experience working with vector databases and understanding of similarity search techniques and vector indexing algorithms
Benefits:
- Medical, Dental, and Vision insurance
- 100% company paid premiums
- Unlimited Paid Time Off
- 26 weeks of 100% paid Parental Leave
- 401(k) plan with 6% employer matching
- Generous company matching on donations to non-profit charities
- Up to $20,000 tuition assistance toward degree programs
- Plentiful snacks, drinks, and regularly catered lunches
- Dog-friendly office
- Bike sharing program memberships
- Compassion leave and elder care leave
- Mentoring and additional learning opportunities