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About This Role
Locations: New York, New York
Job description
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About this role
YOUR TEAM
Aladdin Platform Engineering powers the technology foundation behind BlackRock’s Aladdin platform \- a unified system that connects risk, portfolio management, trading, and operations for investors globally. We build the mission critical hosting platform that enable engineers to develop products and operators to run the platform at scale.
Our teams sit at the center of how Aladdin evolves \- partnering across product, data, and engineering to deliver scalable infrastructure that support mission\-critical workflows for clients worldwide. We develop AI\-first engineering capabilities, using a suite of tools across the development lifecycle to improve developer productivity and reduce friction at scale.
YOUR ROLE AND IMPACT
As an AI\-Enabled Infrastructure and Systems Engineer, you will combine deep infrastructure expertise with modern software engineering practices to design, build, and operate the platforms that power critical business capabilities. You will develop scalable, secure, and resilient solutions while automating their deployment, configuration, and lifecycle management through Infrastructure as Code, software development, and platform engineering principles. Your work will simplify complex operations, improve reliability, and enable teams to deliver technology faster, more consistently, and at greater scale.
You’ll pair strong engineering fundamentals with an AI\-driven approach to solving problems—leveraging automation, intelligent tooling, and emerging AI capabilities to reduce operational toil, accelerate delivery, and enhance the developer experience. As a technical leader, you will help shape platform strategy, influence architectural direction, mentor engineers, and drive the adoption of next\-generation engineering practices that define the future of Aladdin.
YOUR RESPONSIBILITIES
- Architect and build scalable, reliable, and secure infrastructure across multi\-cloud environments (AWS, Azure, GCP) that underpins Aladdin’s platform and application ecosystem
- Design AI focused infrastructure platforms supporting model development, training, evaluation, and inference.
- Lead automation initiatives using Infrastructure as Code (IaC) tools such as Terraform, Ansible, and CloudFormation to support mission\-critical workloads
- Apply AI\-assisted engineering approaches to reduce toil and enhance how software is built, tested, and operated
- Adopt a product\-centric approach, treating internal platforms and automation frameworks as products with clear ownership, lifecycle management, and continuous improvement.
- Collaborate across global teams to solve complex technical challenges and deliver shared platform capabilities
- Contribute to system design, architecture decisions, and technical direction for key components and services
- Own execution and delivery of large\-scale projects, balancing hands\-on technical work with cross\-functional collaboration across engineering, operations, and governance teams.
- Mentor engineers and help foster a collaborative, inclusive, and high\-performing engineering culture
YOU HAVE…
- 5\+ years of experience in infrastructure engineering, building and delivering production\-grade systems
- Proven experience in cloud infrastructure, platform engineering, or systems engineering roles.
- Proficiency with Infrastructure as Code‑as‑code tools (e.g., Terraform, ARM/Bicep, CloudFormation).
- Strong programming or scripting skills (e.g., Python, Java, Golang, or similar).
- Hands\-on expertise with AWS and/or Azure and/or GCP, including Azure ML, Azure Foundry, AWS Bedrock, Google Vertex, as well as cloud compute, networking, storage, and security services.
- Familiarity with modern development practices (testing, version control, CI/CD) and scalable system design
- Exposure to cloud\-native architectures, microservices, or platform engineering concepts
- Experience supporting AI and machine learning workloads, with exposure to managed compute for model training and fine‑tuning, experimentation over large datasets, and end‑to‑end MLOps pipeline flow including data ingestion, training, validation and deployment.
- Interest or experience in applying AI/ML tools or AI\-assisted development to enhance productivity or user experience
- Proven ability to own components end\-to\-end, lead technical work, and collaborate effectively across teams
- Strong problem\-solving, communication, and curiosity \- with an ability to learn quickly in a complex domain
For New York, NY Only the salary range for this position is USD$162,000\.00 \- USD$200,000\.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 \#
R265146
Salary Context
This $162K-$200K 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($181K) sits 17% below the category median. Disclosed range: $162K to $200K.
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
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