Interested in this AI/ML Engineer role at MSCI?
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About This Role
Your Team Responsibilities:
The Principal AI Scientist / Principal AI Engineer at MSCI is a high\-impact, individual contributor role that sits at the intersection of advanced data science, machine learning, and product engineering. Operating within the core product teams, this leader directly shapes the algorithms powering the company’s multi\-billion\-dollar financial platform and products at MSCI.
The role focuses on building production\-grade machine learning models, scaling AI pipelines, and embedding generative AI agents to solve financial workflows for clients worldwide.
Your Key Responsibilities:
- Algorithm \& Model Development
- Advanced ML Application: Apply cutting\-edge techniques in Natural Language Processing (NLP), Causal\-ML, Deep Learning, Reinforcement Learning, and Generative AI (LLMs) to solve real\-world financial problems.
- Experimentation \& Prototyping: Partner directly with software engineers, product managers, and designers to design rapid experiments and build Minimum Viable Products (MVPs) to test out 0 1 AI\-driven experiences.
- Technical Leadership \& Technical Rigor
- Team Stewardship: Lead the technical roadmap for a product area or domain. You will drive end\-to\-end architecture designs for the team’s data science solutions and enforce best practices in code quality, algorithm development, experiences and agentic engineering.
- AI Guardrails \& Evaluation: Build and standardize evaluation methodologies to strictly validate model accuracy, explainability (XAI), and fairness, ensuring compliant and responsible AI practices.
- Reusable Pipeline Infrastructure: Architect robust, end\-to\-end reusable pipelines that smoothly transition algorithmic models from standalone research prototypes directly into stable production\-level services.
- Cross\-Functional Influence \& Mentorship
- Evangelism: Translate complex statistical and algorithmic logic into digestible, business\-focused insights for senior leadership and non\-technical stakeholders.
- Industry \& Academic Connection: Actively track the latest developments in academia and broader tech sectors to proactively integrate emerging AI paradigms into MSCI Products.
- Talent Growth: Provide technical mentorship to junior engineers, fostering a collaborative culture of continuous technical excellence.
Your skills and experience that will help you excel:
- Experience: 10\+ years of cumulative industry experience in a dedicated data science or machine learning capacity.
- Leadership Track Record: 3\+ years acting as a technical lead, steering data science initiatives or managing delivery of mission\-critical ML workflows.
- Deep Math \& ML Core: Expert\-level proficiency across optimization paradigms (e.g., gradient methods, Bayesian optimization) and foundational ML frameworks.
- Technical Stack: Deep hands\-on coding experience in big data environments using general\-purpose languages like Python, Scala, Java, or R, alongside strong SQL capabilities.
- Education: Bachelor’s, Master’s, or Ph.D. in a highly quantitative discipline (such as Computer Science, Statistics, Mathematics, Operations Research, or Economics).
About MSCI: What we offer you* Salary range: $225,000 \- $250,000 / year, plus eligible for annual bonus
- Transparent compensation schemes and comprehensive employee benefits, tailored to your location, ensuring your financial security, health, and overall wellbeing.
- Flexible working arrangements, advanced technology, and collaborative workspaces.
- A culture of high performance and innovation where we experiment with new ideas and take responsibility for achieving results.
- A global network of talented colleagues, who inspire, support, and share their expertise to innovate and deliver for our clients.
- Global Orientation program to kickstart your journey, followed by access to our Learning@MSCI platform, AI Learning Center , LinkedIn Learning Pro and tailored learning opportunities for ongoing skills development.
- Multi\-directional career paths that offer professional growth and development through new challenges, internal mobility and expanded roles.
- We actively nurture an environment that builds a sense of inclusion belonging and connection, including eight Employee Resource Groups. All Abilities, Asian Support Network, Black Leadership Network, Climate Action Network, Hola! MSCI, Pride \& Allies, Women in Tech, and Women’s Leadership Forum.
At MSCI we are passionate about what we do, and we are inspired by our vision – to power better decisions. You’ll be part of an industry\-leading network of creative, curious, and entrepreneurial pioneers. This is a space where you can challenge yourself, set new standards and perform beyond expectations for yourself, our clients, and our industry.
MSCI strengthens global markets by connecting participants across the financial ecosystem with a common language. Our research\-based data, analytics and indexes, supported by advanced technology, set standards for global investors and help our clients understand risks and opportunities so they can make better decisions and unlock innovation. We serve asset managers and owners, private\-market sponsors and investors, hedge funds, wealth managers, banks, insurers and corporates.
MSCI Inc. is an equal opportunity employer. It is the policy of the firm to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, creed, age, sex, gender, gender identity, sexual orientation, national origin, citizenship, disability, marital and civil partnership/union status, pregnancy (including unlawful discrimination on the basis of a legally protected parental leave), veteran status, or any other characteristic protected by law. MSCI is also committed to working with and providing reasonable accommodations to individuals with disabilities. If you are an individual with a disability and would like to request a reasonable accommodation for any part of the application process, please email [email protected] and indicate the specifics of the assistance needed. Please note, this e\-mail is intended only for individuals who are requesting a reasonable workplace accommodation; it is not intended for other inquiries. To all recruitment agencies
MSCI does not accept unsolicited CVs/Resumes. Please do not forward CVs/Resumes to any MSCI employee, location, or website. MSCI is not responsible for any fees related to unsolicited CVs/Resumes. Note on recruitment scams
We are aware of recruitment scams where fraudsters impersonating MSCI personnel may try and elicit personal information from job seekers. Read our full note on careers.msci.com
Salary Context
This $225K-$250K range is above the 75th percentile 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 MSCI, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($237K) sits 11% above the category median. Disclosed range: $225K to $250K.
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.
MSCI AI Hiring
MSCI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $250K - $250K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
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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