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About This Role
Blackstone is the world’s largest alternative asset manager. Blackstone seeks to deliver compelling returns for institutional and individual investors by strengthening the companies in which the firm invests. Blackstone’s over $1.3 trillion in assets under management include global investment strategies focused on real estate, private equity, credit, infrastructure, life sciences, growth equity, secondaries and hedge funds. Further information is available at www.blackstone.com. Follow @blackstone on LinkedIn, X (Twitter), and Instagram.
Job Description:Real Estate – Innovation, Applied AI Engineer
Business Unit:Blackstone Real Estate
Blackstone is a global leader in real estate investing. Blackstone’s real estate business was founded in 1991 and has US $314 billion of investor capital under management. Blackstone is the largest owner of commercial real estate globally, owning and operating assets across every major geography and sector, including logistics, data centers, residential, office and hospitality. Our opportunistic funds seek to acquire undermanaged, well-located assets across the world. Blackstone’s Core+ business invests in substantially stabilized real estate assets globally, through both institutional strategies and strategies tailored for income-focused individual investors including Blackstone Real Estate Income Trust, Inc. (BREIT). Blackstone Real Estate also operates one of the leading global real estate debt businesses, providing comprehensive financing solutions across the capital structure and risk spectrum, including management of Blackstone Mortgage Trust (NYSE: BXMT).
Title: Senior Associate
Location: New York, NY
Responsibilities:
Blackstone Real Estate has one of the richest proprietary real estate data environments in the world. Blackstone Real Estate a seeking an Applied AI Engineer, at the Associate or Senior Associate level, who will work directly with the business to turn that advantage into production-quality, scalable AI tools, workflows and applications that help our teams make better decisions faster.
Primary responsibilities include, but are not limited to:
- Partnering with investment, asset management, portfolio management and other teams to identify high-impact AI and automation opportunities, translate them into clear technical plans, define measurable outcomes, and deploy solutions
- Building applied AI solutions using Blackstone’s proprietary real estate data, including agents, dashboards, data pipelines, analytical applications and workflows that combine structured data, unstructured documents, external information and user feedback
- Working hands-on with Python, SQL, APIs, databases, LLMs, AI agents and enterprise AI tools to deliver reliable and scalable outputs
- Partnering with engineering, data and information security teams to ensure solutions are secure, governed, well-tested and ready for broader deployment
- Communicating progress, risks and recommendations clearly to both technical and non-technical stakeholders, including senior business leaders
Qualifications:
Blackstone seeks to hire individuals who are highly motivated self-starters, work well in team-oriented environments, exercise sound judgment and have demonstrated excellence in prior endeavors. The successful candidate should meet the following qualifications:
- 3+ years of experience in software engineering, data engineering, applied AI, machine learning, systems engineering, forward deployed engineering or a similar technical role
- Undergraduate degree with strong academic record; Computer Science, Engineering, Mathematics, Statistics or a related quantitative field preferred
- Strong technical foundation, including hands-on experience with Python, SQL, APIs, data pipelines, databases and modern software development practices
- Experience building with LLMs, AI agents, retrieval workflows, automation tools or enterprise AI applications
- Real estate, private markets, investment management or financial services experience preferred but not required
- Experience working with real-world data across spreadsheets, documents, databases and enterprise systems, with a focus on accuracy, reliability and business usefulness
- Strong analytical skills and commercial judgment, connecting technical work to investment or operational outcomes
- Excellent written and verbal communication skills, including experience explaining technical concepts in clear language to business stakeholders
The duties and responsibilities described here are not exhaustive and additional assignments, duties, or responsibilities may be required of this position. Assignments, duties, and responsibilities may be changed at any time, with or without notice, by Blackstone in its sole discretion.
Expected annual base salary range:
$130,000 - $200,000 Actual base salary within that range will be determined by several components including but not limited to the individual's experience, skills, qualifications and job location. For roles located outside of the US, please disregard the posted salary bands as these roles will follow a separate compensation process based on local market comparables.
Additional compensation and benefits offered in connection with the role consist of comprehensive health benefits, including but not limited to medical, dental, vision, and FSA benefits; paid time off; life insurance; 401(k) plan; and discretionary bonuses. Certain employees may also be eligible for equity and other incentive compensation at Blackstone’s sole discretion.
Blackstone is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, creed, religion, sex, pregnancy, national origin, ancestry, citizenship status, age, marital or partnership status, sexual orientation, gender identity or expression, disability, genetic predisposition, veteran or military status, status as a victim of domestic violence, a sex offense or stalking, or any other class or status in accordance with applicable federal, state and local laws. This policy applies to all terms and conditions of employment, including but not limited to hiring, placement, promotion, termination, transfer, leave of absence, compensation, and training. All Blackstone employees, including but not limited to recruiting personnel and hiring managers, are required to abide by this policy.
If you need a reasonable accommodation to complete your application, please contact Human Resources at 212-583-5000 (US), +44 (0)20 7451 4000 (EMEA) or +852 3656 8600 (APAC).
Depending on the position, you may be required to obtain certain securities licenses if you are in a client facing role and/or if you are engaged in the following:
- Attending client meetings where you are discussing Blackstone products and/or and client questions;
- Marketing Blackstone funds to new or existing clients;
- Supervising or training securities licensed employees;
- Structuring or creating Blackstone funds/products; and
- Advising on marketing plans prepared by a sales team or developing and/or contributing information for marketing materials.
Note: The above list is not the exhaustive list of activities requiring securities licenses and there may be roles that require review on a case-by-case basis. Please speak with your Blackstone Recruiting contact with any questions.
To submit your application please complete the form below. Fields marked with a red asterisk * must be completed to be considered for employment (although some can be answered "prefer not to say"). Failure to provide this information may compromise the follow-up of your application. When you have finished click Submit at the bottom of this form.
Salary Context
This $130K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Blackstone, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $130K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Blackstone AI Hiring
Blackstone has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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