Interested in this AI/ML Engineer role at BlackRock?
Apply Now →Skills & Technologies
About This Role
Locations: New York, New York
Job description
-------------------
About this role
Are you interested in building innovative technology that shapes the financial markets? Do you like working at the speed of a startup, but want to solve some of the world’s most complex problems? Do you want to work with, and learn from, hands\-on leaders in technology and finance?
At BlackRock, we are looking for Software Engineers who like to innovate and solve complex problems. We recognize that strength comes from diversity, and will embrace your unique skills, curiosity, drive, and passion while giving you the opportunity to grow technically and as an individual.
With over USD $14\+ trillion of assets we have an exceptional responsibility: our technology empowers millions of investors to save for retirement, pay for college, buy a home, and improve their financial wellbeing.
Being a developer at BlackRock means you get the best of both worlds: working for one of the most advanced financial companies and being part of a software development team responsible for next generation technology and solutions.
What is Aladdin and Aladdin Engineering (AE)?
You will be working on BlackRock's investment operating system Aladdin. Aladdin is used both internally and externally by many financial institutions. Aladdin combines sophisticated risk analytics with comprehensive portfolio management, trading and operations tools on a single platform to power informed decision\-making and create a connective tissue for thousands of users investing worldwide.
Our development team sits inside Aladdin Engineering. We collaboratively build the next generation of technology that changes the way information, people, and technology intersect for global investment firms. We build and package tools that manage trillions in assets and supports millions of financial instruments. We perform risk calculations and process millions of transactions for thousands of users every day worldwide!
The Analytics and Compliance Engineering team is responsible for developing and maintaining tools to ensure that organizations using Aladdin® comply at different stages of the asset management process. This includes ensuring that clients follow guidelines established internally, by themselves, and by the market regulators. The system accomplishes this through high\-throughput rule\-based compliance engines, which sit at the heart of the investment system and process millions of calculations per day.
As a senior member of the Analytics and Compliance Engineering organization, you will lead the transformation of compliance technology through Artificial Intelligence, Agentic Systems, and Intelligent Automation. You will drive the adoption of AI\-native engineering practices, leveraging LLMs, AI agents, knowledge retrieval systems, and advanced analytics to build the next generation of compliance, risk management, and regulatory technology platforms.
### What We Are Looking For
AI\-First Innovator
- Leverage Generative AI, Agentic AI, intelligent automation, and predictive analytics to redefine how compliance solutions are designed, developed, and operated.
Identify high\-impact opportunities where AI can improve developer productivity, regulatory insight generation, operational efficiency, and decision\-making.
*
Tenacious Problem Solver
- Thrive in a fast\-paced environment solving complex regulatory, data, and engineering challenges at global scale.
- Navigate ambiguity and rapidly evolving AI technologies while maintaining strong governance and operational excellence.
Strategic \& Creative Thinker
- Evaluate emerging AI technologies, foundation models, agent frameworks, RAG architectures, vector databases, and distributed systems to determine optimal solutions.
- Balance innovation, explainability, risk, and compliance requirements when deploying AI\-powered capabilities.
Collaborative AI Leader
- Partner closely with engineering, product management, compliance, legal, risk, and business stakeholders to deliver AI\-enabled outcomes.
Foster an AI\-learning culture focused on experimentation, responsible AI adoption, and continuous improvement.
*
Continuous Learner
- Remain current on advances in Generative AI, AI Agents, LLM orchestration, cloud\-native AI infrastructure, and machine learning techniques.
- Champion modern AI engineering practices across the organization.
Responsibilities
- Drive the strategic adoption of AI across compliance workflows, regulatory monitoring, reporting, and rule management capabilities.
- Define and execute an AI transformation roadmap aligned with business priorities and regulatory requirements.
- Design highly available, fault\-tolerant, cloud\-native systems capable of supporting AI\-driven workloads at enterprise scale.
- Own projects priorities, deadlines and deliverables using AGILE methodologies.
- Significantly contribute to development of Aladdin’s global, multi\-asset trading platform
- Provide impact and expertise as a senior individual contributor in building various capabilities of the compliance system, including developing a next\-gen compliance rule evaluation engine and compliance violation management at scale.
- Work with product management and business users to define the roadmap for the product.
- Design and develop innovative solutions to complex problems, identifying issues and roadblocks.
Be a leader with vision and a partner in brainstorming solutions for team productivity, efficiency, guiding and motivating developers.
*
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Data Science, Artificial Intelligence, or a related quantitative discipline.
- Hands\-on experience designing and deploying:
+ Generative AI solutions
+ LLM\-powered applications
+ Agentic AI workflows
+ RAG architectures
+ Vector search platforms
+ AI orchestration frameworks (LangChain, LangGraph, Semantic Kernel)
+ AI observability and evaluation frameworks
+ Knowledge of prompt engineering, context engineering, model evaluation, and AI governance practices
- Over 5 years of hands\-on experience in Java.
- In\-depth understanding of concurrent programming and designing high throughput, high availability, fault\-tolerant distributed applications.
- Expertise in building distributed applications using SQL and/or NoSQL technologies such as MSSQL, Flexible JSON document data store, Snowflake, or In\-memory data stores for caching and real\-time analytics.
- Strong interest in distributed systems, infrastructure services, cloud technology and AI/ML techniques.
- Prior experience with message broker technology such as Real\-time distributed event streaming platform or gRPC.
- Excellent analytical and software architecture design skills, with an emphasis on test\-driven development.
- Strong communication and presentation skills, both written and verbal.
For New York, NY Only the salary range for this position is USD$162,000\.00 \- USD$215,000\.00 . Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits. BlackRock operates a pay\-for\-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance.
Our benefits
To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.
Our hybrid work model
BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.
Guidance on AI use for candidates
At BlackRock, AI has long been part of how we work – enhancing decision\-making, improving operations, and helping us deliver better outcomes for clients. We encourage candidates to use AI thoughtfully to learn, prepare, and work more effectively; but during our interview process, we want to focus on getting to know you through your own experiences, thinking, and judgment. To support you, we’ve provided guidance( opens in new window) on when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.
About BlackRock
At BlackRock, we are all connected by one mission: to help more and more people experience financial well\-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.
This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.
To learn more about BlackRock, please visit Careers.BlackRock.com( opens in new window). We also encourage you to get to know us on LinkedIn( opens in new window), Instagram( opens in new window), YouTube( opens in new window), X( opens in new window), and TikTok( opens in new window).
BlackRock is proud to be an equal opportunity workplace. We are committed to equal employment opportunity to all applicants and existing employees, and we evaluate qualified applicants without regard to race, creed, color, national origin, sex (including pregnancy and gender identity/expression), sexual orientation, age, ancestry, physical or mental disability, marital status, political affiliation, religion, citizenship status, genetic information, veteran status, or any other basis protected under applicable federal, state, or local law. View the EEOC’s Know Your Rights poster and its supplement( opens in new window) and the pay transparency statement( opens in new window).
BlackRock is committed to full inclusion of all qualified individuals and to providing reasonable accommodations or job modifications for individuals with disabilities. If reasonable accommodation/adjustments are needed throughout the employment process, please email Disability.Assistance@blackrock.com( opens in new window). All requests are treated in line with our privacy policy( opens in new window).( opens in new window)
BlackRock will consider for employment qualified applicants with arrest or conviction records in a manner consistent with the requirements of the law, including any applicable fair chance law.
Job Requisition \#
R264222
Salary Context
This $162K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BlackRock, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($188K) sits 14% below the category median. Disclosed range: $162K to $215K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BlackRock AI Hiring
BlackRock has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in New York, NY, US. Compensation range: $162K - $387K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.