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About This Role
Locations: New York, New York
Job description
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About this role
BlackRock is one of the world’s leading providers of investment, advisory, and risk management solutions, including Aladdin, our investment and risk management technology. Aladdin is a comprehensive technology platform used by BlackRock and delivered to our clients to unify the investment management process across public and private markets. It integrates risk, investment, and client management processes through a common data language, enabling scale, insights, and business transformation. At BlackRock, the Aladdin team comprises the Aladdin Client Business, Aladdin Wealth Tech, Aladdin Product, Aladdin Data, and Aladdin Engineering.
Join our AI Platform Engineering team as part of Aladdin Engineering and be at the forefront of financial technology innovation. Aladdin Engineering is the team that designs, builds, and runs Aladdin. Within Aladdin Engineering, there are multiple engineering teams focused on building the different products and platform services for Aladdin. As part of the AI Platform Engineering team, you will play a crucial role in shaping the AI ecosystem across the firm and significantly influencing the Aladdin application ecosystem.
We are looking for highly motivated and determined engineers to set and drive a clear AI Software Strategy, delivering cohesive AI experiences across the firm. At BlackRock, you will have the opportunity to work with some of the brightest minds in the industry, leveraging your insights and expertise to advance AI Platform Engineering. We value diversity and believe it is the key to our success, ensuring that your unique skills, curiosity, and passion are nurtured. Join us and grow both technically and personally while working at one of the most recognized financial companies in the world as part of an AI software development team.
Key Responsibilities:
- Design, develop, and maintain the next generation of scalable AI platform for the world's best investment management technology platform.
- Implement and manage Kubernetes clusters for deploying AI models.
- Build platform abstractions to manage cloud\-native infrastructure across AWS, GCP, or Azure environments.
- Build and maintain automated pipelines for continuous training, testing, and deployment of machine learning models, with integrated enterprise concerns.
- Ensure the security and compliance of the platform.
- Troubleshoot and resolve issues related to platform performance and reliability.
- Refine business and functional requirements and translate them into scalable technical designs.
- Apply quality software engineering practices throughout the software development lifecycle.
- Work with team members in a multi\-office, multi\-country environment.
- Stay updated with the latest trends and technologies in AI and cloud engineering.
Requirements:
- B.S./M.S. degree in Computer Science, Engineering, or a related subject area.
- 10\+ years of experience in software and platform engineering.
- Proficiency in designing and building scalable APIs and microservices.
- Strong proficiency in Kubernetes, including Helm charts, Kustomize, and custom resource definitions (CRDs).
- Hands\-on experience with cloud platforms such as AWS, GCP, or Azure.
- Expertise in containerization technologies (Docker, containerd).
- Experience in CI/CD tools (Jenkins, GitHub Actions, ArgoCD).
- Knowledge of infrastructure such as code (IaC) tools like Terraform or CloudFormation.
- Solid understanding of networking concepts, security policies, and API gateways in cloud environments.
- Proficiency in production\-grade programming languages such as Rust and C\+\+.
- Decent understanding of distributed systems, cluster orchestration and management.
- Good knowledge of data science tools (e.g PyTorch, Jax, Numpy) and programming languages such as Python.
- Experience with monitoring tools (Prometheus, Grafana).
- Experience working in Agile development teams with excellent collaboration skills.
- Grit in the face of technical obstacles.
Nice to have:
- Building SDKs or client libraries to support API consumption.
- Knowledge of distributed data processing frameworks (Spark, Dask).
- Understanding of GPU orchestration and optimization in Kubernetes.
- Familiarity with MLOps and ML model lifecycle pipelines.
- Experience with AI model training and fine\-tuning.
- Familiarity with event\-driven architecture and messaging frameworks like Kafka.
- Experience with NoSQL datastores like Cassandra.
For New York, NY Only the salary range for this position is USD$220,000\.00 \- USD$275,000\.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 [email protected] (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 \#
R264770
Salary Context
This $220K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($247K) sits 38% above the category median. Disclosed range: $220K to $275K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
BlackRock AI Hiring
BlackRock has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $215K - $275K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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