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About This Role
The Bloomberg Enterprise Data business is experiencing significant growth. Within this organization, Data License Plus enables the distribution of an unparalleled breadth and depth of enterprise content to the world's leading market participants. We are expanding this platform to support AI‑driven and agentic client workflows, helping customers build the next generation of data‑powered solutions.
What’s in it for you
We are looking for a Technical Product Manager to define and execute on the vision for integrating our broad range of high quality financial data into client agentic workflows. In this role, you will own the end‑to‑end client experience and co‑own the roadmap for our Data Solutions for Agentic AI. You will design and deliver new features, integrations, and capabilities that make it easier for clients to build AI‑driven solutions using Bloomberg data and to drive the growth of our business. You’ll work closely with Product, Engineering, and other partners in a highly cross‑functional, high‑impact environment, balancing deep technical understanding with strong business judgement.
We’ll trust you to
- Develop a long\-term technical strategy, roadmap, framework, design patterns, for delivering data to generative AI systems
- Build the next generation of financial data products for agentic AI solutions
- Partner closely with product and engineering teams to deliver the defined business outcomes
- Interact with customers to understand their workflows and technical requirements, and develop a deep understanding of an ideal solution
- Foster a culture of collaboration with your Engineering and UX counterparts
- Prioritize, plan, make decisions, ask the right questions, and understand how to plan for and manage resources effectively
- Take the initiative, be proactive, innovate, advocate, understand the priorities for the business, and do what’s best for Bloomberg
- Define an effective go\-to\-market strategy
You’ll need to have
- 5\+ years of experience in technical product management
- Experience working with developer teams and building AI or other data\-intensive applications
- Experience with emerging GenAI standards and protocols such as MCP or A2A
- Breadth of knowledge or familiarity with GenAI platforms, MLOps platforms, AI toolkits
- Organizational and communication skills to effectively coordinate and work with engineers, subject matter experts, other product managers, and senior management
- A product\-driven focus and track record of shaping business strategy and roadmaps for technical products
- Experience working with customers to identify and understand use cases and problems we should be solving
We’d love to see
- A degree in Computer Science, Engineering, Data Science, HCI, AI, similar field of study or equivalent work experience
- Applied experience with machine learning systems and large language models, including their operational and scaling requirements
- Experience with public cloud providers such as AWS, GCP, or Azure
- Working knowledge of capital markets data, market structure, or buy‑side/sell‑side workflows
Salary Range \= 140,000 \- 295,000 USD Annual \+ Benefits \+ Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) \+match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
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Accommodations
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Bloomberg provides reasonable adjustment/accommodation to individuals with disabilities. Please tell us if you require a reasonable adjustment/accommodation to apply for a job. Examples of reasonable adjustment/accommodation include but are not limited to making a change to the application process or work procedures, providing documents in an alternate format or using specialized equipment. To request an adjustment/accommodation to apply for a job, please email AMER\_recruit@bloomberg.net (Americas), EMEA\_recruit@bloomberg.net (Europe, the Middle East and Africa), or APAC\_recruit@bloomberg.net (Asia\-Pacific), based on the region you are submitting an application for. We may share your information with a third party provider of accommodations services who may use this information to reach out to you for the purposes of accommodating your application.
Equal Opportunity
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Bloomberg is an equal opportunity employer and prohibits discrimination in employment. It is Bloomberg’s policy to provide equal opportunity and access for all persons, and the Company is committed to attracting, retaining, developing, and promoting the most qualified individuals without regard to age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, self\-identified or perceived sex, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy, childbirth or related medical conditions, or parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law (each, a “Protected Characteristic”). Bloomberg prohibits treating applicants or employees less favorably in connection with the terms and conditions of employment, in all phases of the employment process, because of one or more Protected Characteristics.
Salary Context
This $140K-$295K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Bloomberg, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $140K to $295K.
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.
Bloomberg AI Hiring
Bloomberg has 5 open AI roles right now. They're hiring across AI Product Manager, Data Engineer, AI/ML Engineer. Positions span New York, NY, US, Princeton, NJ, US. Compensation range: $190K - $350K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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