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Bloomberg's AI is transforming how investment professionals discover information, run analysis, automate workflows and collaborate across their organizations. ASKB has quickly become a primary conversational interface for Bloomberg Terminal users — letting users interact with Bloomberg's data, analytics, news and applications in natural language, powered by specialized AI agents and grounded in three principles: accuracy, transparency and trusted content.
As firms increasingly look to deploy AI at an enterprise level, Bloomberg is bringing enterprise\-grade administration, governance and collaboration to its AI capabilities — so firms can adopt AI across teams and departments while maintaining security, governance and trust. This is one of Bloomberg's most exciting product areas, combining AI, enterprise software and workflow transformation.
The role
We're seeking an experienced product leader to build Bloomberg's enterprise AI business across privacy, capacity management, team sharing/collaboration with firm level data and firm level admin and access controls. As Team Lead for Enterprise AI, you'll own the product vision, overall strategy, commercials and pricing for how firms adopt, govern and scale AI across their organizations — and you'll lead and grow a team of product managers who deliver the capabilities that make firm\-level AI adoption possible: firm privacy, AI capacity management, team sharing and collaboration, and central firm administration.
You'll be the single leader accountable for turning a per\-seat assistant into a firm\-wide AI platform — setting direction, holding the pillars together as one coherent product, owning the commercial model, and expanding the business into new cohorts such as research, private credit and insurance. You'll represent the product to senior leadership and enterprise clients, and be measured on the firms won, revenue and firm\-wide adoption it delivers.
Own the business. As the team leader of Bloomberg's enterprise AI product, you'll set the vision and strategy, own commercials and pricing end\-to\-end, and be accountable for the P\&L levers behind it — building a commercial model that lets regulated firms adopt ASKB at scale across ASKB\+, ASKB Teams and ASKB Enterprise, consistent with Bloomberg's other enterprise products.
Lead the team. You'll lead, mentor and grow a team of product managers, each owning part of the enterprise offering — the privacy controls firms need to say yes, the capacity model and pricing that scale with AI usage, the sharing and collaboration that turn per\-seat AI into a firm\-wide platform, and the central admin tooling that lets a firm onboard and govern it. You'll hold these to one product vision, set priorities across them, and unblock delivery so they ship in parallel rather than one at a time.
You'll work directly with clients to understand how firms want to deploy AI, translate those needs into detailed Product Requirement Documents and delivery requirements, and partner across Product, UX, Engineering, Commercial and Sales to deliver solutions that accelerate adoption.
We'll trust you to
- Own the product vision, overall strategy and roadmap for Bloomberg's enterprise AI solutions, holding privacy, capacity, collaboration and administration together as one coherent enterprise product.
- Own commercials and pricing strategy end\-to\-end — packaging, pricing and go\-to\-market for the enterprise AI tiers (ASKB\+, ASKB Teams, ASKB Enterprise), consistent with Bloomberg's other enterprise products, and be accountable for the revenue it drives.
- Lead, mentor and grow the team of product managers responsible for firm privacy, AI capacity management, team sharing and collaboration, and central firm administration — setting direction, prioritising across the pillars and unblocking delivery.
- Own the enterprise tiering commercials — capacity as both a product and a pricing lever, tiered and pooled subscriptions, and the cost, usage and telemetry data behind every pricing decision.
- Develop the enterprise administration, governance and privacy capabilities regulated financial institutions require — admin tooling, user controls, entity\-level privacy options, data residency, auditability, capacity management and content governance.
- Define how teams collaborate around AI — shared context, projects, agents, workflows, connectors and firm knowledge, both inside the firm and outward to non\-Bloomberg users and third\-party applications.
- Grow the business into new cohorts — expand beyond research into adjacent front\-office segments such as private credit and insurance.
- Work directly with enterprise clients to understand AI adoption strategies, validate concepts, and translate needs into detailed PRDs and requirements for delivery teams.
- Define KPIs and adoption metrics for Bloomberg's enterprise AI offerings, using telemetry and customer insight to shape direction.
- Communicate product vision, roadmap and commercial performance to senior leadership, building alignment across Product, Engineering, Commercial and Sales.
You'll need to have
- Proven product leadership experience within enterprise software, SaaS or AI platforms, including leading initiatives from concept through launch — and, ideally, leading or mentoring other product managers.
- Strong experience owning commercials and pricing strategy for enterprise or SaaS products.
- Hands\-on experience with AI / LLM\-based products or enterprise productivity platforms.
- A solid understanding of enterprise software administration, governance and security.
- Experience setting product vision and strategy across multiple workstreams and holding them to a coherent whole.
- Experience authoring Product Requirement Documents and managing complex cross\-functional roadmaps.
- Strong analytical skills — using telemetry, adoption metrics, and cost and usage data to shape product and pricing direction.
- Outstanding written and verbal communication, with the ability to influence senior stakeholders across Product, Engineering, Sales and Executive Management.
- Experience working directly with enterprise clients to define product strategy and validate solutions.
- The ability to balance strategic thinking and commercial ownership with detailed execution in a fast\-moving environment.
We'd love to see
- Experience leading a product team or owning a product line end\-to\-end, including its commercials.
- Experience with the Bloomberg Terminal or Bloomberg Enterprise products.
- Experience building enterprise AI and collaboration solutions.
- Familiarity with SaaS commercial models, including usage\-based or capacity\-based pricing.
- Experience with enterprise identity, governance or administration platforms.
- Knowledge of enterprise integrations — APIs, connectors, SharePoint, Snowflake, Databricks or MCP frameworks.
- Domain fluency in financial markets and how regulated institutions evaluate technology.
Salary Range \= 235,000 \- 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 $235K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($292K) sits 36% above the category median. Disclosed range: $235K to $350K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bloomberg AI Hiring
Bloomberg has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in New York, NY, US. Compensation range: $180K - $350K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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