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About This Role
Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock \- from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology \- quickly and accurately. We apply problem\-solving skills to identify workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.
Our Team:
Data AI contributes to the building of Bloomberg’s AI\-enhanced products at scale by curating model training data and enhancing how our internal processes use AI. We provide evaluation and annotation frameworks connecting natural language processing and human judgment in order to elevate the quality, intelligence, and usability of the data that drives our products.
By investing in AI at a strategic level, we expand our practice of engaging with AI to one that is embedded across Data. Our internal processes to take advantage of new AI technologies and strengthen Data’s role in providing robust domain expertise and influential data artifacts to Bloomberg’s products. As a result our clients will continue to have high quality data and access to new types of datasets.
What’s The Role:
The Data Product Owner serves as the strategic leader responsible for transforming business operations through data and AI. By aligning product, engineering, and operational partners around a shared vision, they define and deliver the data capabilities that enable intelligent automation, operational scale, and continuous improvement.
This role goes beyond managing requirements to shape how the organization uses data as a strategic asset. Through workflow optimization, process simplification, and the thoughtful application of AI, the Data Product Owner drives solutions that increase efficiency, improve quality, and create sustainable business value.
Success in this role is measured by the organization's ability to operationalize data and AI at scale, turning complex business challenges into streamlined, reliable, and measurable outcomes.
We’ll Trust You To:
- Own and evolve scalable frameworks and sophisticated strategies for instruction and evaluation task design, ensuring datasets remain fit\-for\-purpose for complex Generative AI behaviors.
- Align data frameworks and evaluation strategies with overarching product objectives to guarantee trustworthy, consumable intelligence that supports actionable user decisions.
- Act as the primary multi\-functional liaison, driving alignment between Product, Engineering, and Data teams to translate technical complexities into actionable insights.
- Partner with multi\-functional teams to define product\-aligned requirements and reusable evaluation rubrics, ensuring outcomes meet rigorous Data Quality standards.
- Drive the strategic evolution of our evaluation infrastructure by pioneering reusable, automated frameworks that consistently accelerate multi\-functional product delivery.
You’ll Need to Have:
- Bachelor’s degree or equivalent experience in Finance, Business, Economics, Accounting, STEM or degree\-equivalent qualifications
- A minimum of four years of demonstrated experience in data management concepts, including data quality, modeling, and random sampling
- Extensive experience using data visualization tools such as Tableau or Qlik Sense to communicate sophisticated results to partners in a clear, concise manner
- Demonstrable experience in Data Profiling/Analysis using tools such as Python, R, or SQL
- Past project/experience analyzing financial datasets or demonstrable experience working on financial market concepts
- A logical approach to problem\-solving with the ability to resolve complex annotation and data\-architectural challenges
- Keen interest in and familiarity with generative AI frameworks and the requirements of Agentic AI
- Excellent stakeholder management and project leadership skills, with a demonstrable ability to evaluate design trade\-offs and seamlessly translate technical complexities between Engineering, Product, and Data teams.
- Experience in data management concepts such as data quality, data modeling, and data engineering
We’d Love to See:
- DAMA CDMP or DCAM certifications
- Experience in using Bloomberg Data, Bloomberg Terminal, and/or enterprise financial data products
- Interest in solving problems and developing data\-driven methodologies for high precision \& high recall anomaly detection
- Past project experience using Agile/Scrum methodologies to manage complex data lifecycles
- Experience customizing or developing annotation interfaces using Javascript or HTML.
Salary Range \= 110,000 \- 190,000 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 $110K-$190K range is below 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 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 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($150K) sits 31% below the category median. Disclosed range: $110K to $190K.
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
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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