Interested in this AI/ML Engineer role at Bloomberg?
Apply Now →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 Product Management Lead to build a team of Technical Product Managers who will 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 be accountable for managing a group of product managers driving product strategy, technical planning, prioritization, and delivery oversight. 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
- Lead and develop a team of product managers, raising the bar on product thinking, execution, and accountability.
- 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
- 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
- Evaluate and respond to the rapid pace of innovation in the industry
- Communicate strategy, progress, and risks effectively to senior leadership.
You’ll need to have
- 7\+ years of product management experience, including 3\+ years of leadership of teams
- Proven experience operating within complex, cross\-functional environments involving Product, Engineering, and Data organizations.
- Experience working with developer teams and building AI or other data\-intensive applications
- 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
- Bachelor’s degree or equivalent practical experience
We’d love to see
- Strong technical experience with emerging GenAI standards and protocols such as MCP or A2A
- Working knowledge of capital markets data, market structure, or buy‑side/sell‑side workflows
Salary Range \= 180,000 \- 350,000 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\[email protected] (Americas), EMEA\[email protected] (Europe, the Middle East and Africa), or APAC\[email protected] (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 $180K-$350K range is above the 75th percentile 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 Bloomberg, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($265K) sits 21% above the category median. Disclosed range: $180K to $350K.
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/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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