Machine Learning Engineer
US - MA - CAMBRIDGE 44-48 BRATTLE ST, United States
Full Time Mid-level / Intermediate USD 150K - 190K
S&P Global
We provide Essential Intelligence: a combination of the right data, connected technologies and experts to enable our customers to make decisions with conviction.Kensho is S&P Global’s hub for AI innovation and transformation. With expertise in Machine Learning and data discovery, we develop and deploy novel solutions for S&P Global and its customers worldwide. Our solutions help businesses harness the power of data and Artificial Intelligence to innovate and drive progress. Kensho's solutions and research focus on speech recognition, entity linking, document extraction, automated database linking, text classification, natural language processing, and more.
The Vector Team at Kensho is focused on designing and deploying production-grade machine learning systems that power our next-generation retrieval-augmented generation (RAG) pipelines. We specialize in building robust retrieval systems, scalable embedding infrastructure, and tightly integrated LLM pipelines that leverage unstructured data sources.
Our mission is to make complex unstructured data easily discoverable and actionable by building intelligent, retrieval-driven systems that enhance enterprise search, question answering, deep research, report generation, and knowledge discovery experiences across S&P Global platforms.
We are 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.
Kensho states that the anticipated base salary range for the position is 150k - 190k. In addition, this role is eligible for an annual incentive bonus and equity plans. At Kensho, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.
What You’ll Do:
Design and implement end-to-end RAG pipelines that integrate 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, 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, from initial technical design to seamless implementation
Who You'll Need:
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, including designing, shipping, and maintaining production systems
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, such as Langchain, LLamaIndex, etc
Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation
Experience working with vector databases (e.g., PGVector, OpenSearch, Pinecone) and understanding of similarity search techniques and vector indexing algorithms
Demonstrated effective coding, documentation, collaboration, and communication habits
Strong problem-solving skills and a proactive approach to addressing challenges
Ability to adapt to a fast-paced and dynamic work environment
Technologies We Love:
ML: PyTorch, Transformers, HuggingFace, LangChain, LlamaIndex
Tools/Toolkits: LabelBox, Weights & Biases, OpenSearch, PGVector, LiteLLM
Techniques: RAG, Prompt Engineering, Information Retrieval, Data Embedding
Deployment: Airflow, Docker, Kubernetes, Jenkins, AWS
At Kensho, we pride ourselves on providing top-of-market benefits, including:
Medical, Dental, and Vision insurance
100% company paid premiums
Unlimited Paid Time Off
26 weeks of 100% paid Parental Leave (paternity and maternity)
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, plus up to $4,000/year for ongoing professional education such as industry conferences
Plentiful snacks, drinks, and regularly catered lunches
Dog-friendly office (CAM office)
Bike sharing program memberships
Compassion leave and elder care leave
Mentoring and additional learning opportunities
Opportunity to expand professional network and participate in conferences and events
Recruitment Fraud Alert:
If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre-employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here.
We are an equal opportunity employer that welcomes future Kenshins with all experiences and perspectives. Kensho is headquartered in Cambridge, MA, with an additional office location in New York City. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
Tags: Airflow ASR AWS Classification Computer Science Docker Engineering HuggingFace Jenkins Kubernetes LangChain LLMs Machine Learning NLP OpenSearch Pinecone Pipelines Prompt engineering Python PyTorch RAG Research Transformers Unstructured data Weights & Biases
Perks/benefits: Career development Conferences Equity / stock options Health care Medical leave Parental leave Pet friendly Salary bonus Team events Unlimited paid time off
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