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About This Role
Machine Learning Engineer II
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- New York, New York; Cambridge, Massachusetts
- Business Management
- 330645
Job Description
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Kensho is S\&P Global’s hub for AI innovation and transformation. With expertise in machine learning, natural language processing, and data discovery, we develop and deploy novel solutions to innovate and drive progress at S\&P Global and its customers worldwide. Kensho's solutions and research focus on business and financial generative AI applications, agents, data retrieval APIs, data extraction, and much more.
At Kensho, we hire talented people and give them the autonomy and support needed to build amazing technology and products. We collaborate using our teammates' diverse perspectives to solve hard problems. Our communication with one another is open, honest, and efficient. We dedicate time and resources to explore new ideas, but always rooted in engineering best practices. As a result, we can innovate rapidly to produce technology that is scalable, robust, and useful.
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.
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 140k \- 180k. 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 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, 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., PostgreSQL/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
- Tools/Toolkits:Claude Code, Weights \& Biases, OpenSearch, PostgreSQL/PGVector, LiteLLM
- Techniques: Agentic Search, Prompt Engineering, Information Retrieval, Data Embedding, AI agent evaluation
- Deployment: Airflow, Docker, Kubernetes, Jenkins, AWS, Github Action
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 [email protected]. 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.
Job ID: 330645
Posted On: 2026\-08\-11
Location: New York, New York, United States
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At S&P Global, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($160K) sits 26% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
S&P Global AI Hiring
S&P Global has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $350K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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