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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.
Blackstone Technology Innovations Profile:
Blackstone Technology and Innovations (BXTI) is the technology team at the core of each of Blackstone’s businesses and new growth initiatives. Serving both internal and external clients, we work to build the next generation of systems that manage risk, create efficiency, and improve transparency within the firm and across our broad community of investors and portfolio companies.
BXTI is fast paced and entrepreneurial – our open, iterative design processes and rapid pace of development mean that everyone on the team has the opportunity to make an impact from day one. We are problem solvers who can take projects from idea to implementation. We believe in active mentoring and developing excellence. We collaborate to find the best answers for our customers and for Blackstone. We are critical to the firm maintaining its competitive edge.
Your Team and Role:
Blackstone’s Security Engineering (SecEng) Team is responsible for enabling secure software delivery across the firm by identifying, assessing, and reducing technology risk while maintaining development velocity. As Blackstone rapidly expands its use of AI, LLM, machine learning platforms, and AI\-enabled software, the SecEng team plays a critical role in ensuring these systems are designed, built, and operated securely.
The Security Engineer – AI \& Software Security role focuses on securing AI systems, platforms, and use cases across the firm. This includes working closely with engineering, data science, platform, and product teams to embed security into the AI software development lifecycle, from design through deployment and operation.
This role is highly cross\-functional and execution\-oriented. You will perform security reviews, threat modeling, code review, penetration testing, and secure design for AI\-enabled applications and supporting platforms. You will also help define scalable security patterns and controls that allow teams to safely build and deploy AI solutions in cloud\-native environments.
You will join a collaborative team of security and software engineers responsible for evolving how Blackstone approaches application, cloud, and AI security as the firm continues to modernize its technology stack.
Responsibilities:
- Serve as a security engineering partner for AI\-enabled applications, machine learning platforms, and data\-driven systems across Blackstone.
- Perform architecture and design reviews for AI systems, including model pipelines, inference services, data flows, and supporting cloud infrastructure.
- Conduct secure code reviews for software and services that integrate AI//LLM/ML capabilities, with a focus on identifying security flaws, misuse cases, and unsafe patterns.
- Lead and execute penetration testing and adversarial testing activities for AI\-enabled applications and APIs, including abuse scenarios unique to AI systems.
- Develop and maintain threat models for AI systems, addressing risks such as data poisoning, model theft, prompt injection, insecure model deployment, and unauthorized access.
- Partner with engineering and data science teams to embed secure\-by\-design principles into AI development workflows, CI/CD pipelines, and platform services. • Help define and standardize security controls, guardrails, and reference architectures for applied AI use cases in cloud\-native environments.
- Work with platform and cloud teams to ensure AI workloads are securely deployed using containers, Kubernetes, and managed cloud services.
- Translate complex AI security risks into clear, actionable guidance for technical and non\-technical stakeholders.
- Contribute to security risk reduction initiatives by identifying systemic AI and application security issues and driving remediation at scale.
- Assist with security incident response and investigations related to AI\-enabled systems, including post\-incident reviews and control improvements.
- Mentor and support junior engineers, helping grow security engineering capabilities across the team.
- Stay current with emerging AI security threats, industry best practices, and regulatory considerations, applying them pragmatically within the enterprise.
Qualifications:
- A minimum of 6 years of progressive experience in one or more of the following:
+ Software engineering or security engineering, with strong proficiency in languages such as Python, Java, Go, or similar
+ Performing security reviews, code reviews, and design assessments for complex software systems
+ Designing and building resilient, well\-documented systems that reduce operational and security risk
+ Working closely with application, platform, DevOps, and infrastructure teams to integrate security into development lifecycles • Managing day\-to\-day security engineering execution, including handling requests, reviews, and remediation guidance
+ Application security and cloud security, including identification and mitigation of software and infrastructure risks Cloud\-native architectures, with a strong preference for AWS, containers, and Kubernetes
+ Infrastructure\-as\-code (IaC), with hands\-on experience using Terraform • Communicating security risks and mitigation strategies effectively to non\-security stakeholders
- A minimum of 2 year of experience in one or more of the following areas:
+ Securing AI/ML platforms, pipelines, or AI\-enabled applications
+ Threat modeling or risk assessment for data\-driven or model\-based systems
+ Multi\-cloud architecture and security integration
- A minimum of Bachelor’s degree (or foreign equivalent) in Computer Science, Cybersecurity, Engineering, or a related field
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:
$200,000 \- $225,000
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 $200K-$225K 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 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 $218,750 based on 3,817 positions with disclosed compensation. Disclosed range: $200K to $225K.
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.
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: $225K - $225K.
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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