Interested in this AI/ML Engineer role at Bloomberg?
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Bloomberg’s Chief Risk Office plays a central role in ensuring that innovation is pursued responsibly across our global operations. As AI becomes increasingly embedded across products, platforms, and internal processes, the Chief Risk Office is building practical advisory capabilities to help teams identify, understand, and manage AI risks throughout the AI lifecycle.
What’sthe role?
We are seeking an AI Risk Technical Advisor to support Bloomberg’s enterprise AI risk management program. This person will review AI use cases, support risk assessments, and advise on control expectations around AI systems, models, and data – in coordination with technical and business teams, in order to help ensure AI systems continue to be developed and used responsibly.
This role is ideal for someone with a strong foundation as an AI technologist, and/or in technology risk, data risk, information security risk, AI/ML, model governance, privacy, or\+ compliance who wants to work at the intersection of AI innovation and enterprise risk management.
We’lltrust you to:
*AI Risk Assessment and Advisory*
- Support AI risk assessments for AI and generative AI use cases across products, platforms, internal tools, and third\-party solutions.
- Evaluate risks related to bias, explainability, hallucination, model drift, robustness, privacy, security, data quality, intellectual property, transparency, and human oversight.
- Help determine appropriate risk tiering, documentation, control requirements, approvals, and monitoring expectations for AI use cases.
- Provide practical guidance to teams on responsible AI requirements, governance processes, and risk mitigation options.
- Escalate complex or higher\-risk issues to senior AI risk leadership and governance forums.
*Framework Implementation*
- Help implement and refine Bloomberg’s AI risk management framework, including inventory, classification, risk tiering, assessment workflows, control expectations, and reporting processes.
- Develop and maintain templates, checklists, guidance documents, FAQs, and training materials to support consistent AI risk reviews.
- Assist with testing and refining governance processes to make them scalable, efficient, and aligned with how teams build and deploy AI.
- Support monitoring of key risk indicators, issue trends, remediation plans, and control effectiveness.
*Cross\-Functional Collaboration*
- Partner with Technology, Product, Legal, Compliance, CISO, Privacy, Data, Procurement, and business stakeholders to support responsible AI adoption.
- Coordinate with teams to gather information, resolve open questions, document decisions, and track follow\-ups.
- Participate in AI risk working groups, governance forums, and cross\-functional discussions.
- Support third\-party AI reviews, including sourcing, onboarding, integration, and ongoing oversight.
*Enablement and Continuous Improvement*
- Support AI risk training, awareness, and culture\-building across the firm.
- Monitor developments in AI technology, AI regulation, and responsible AI practices, and help incorporate those developments into program materials.
- Identify opportunities to improve advisory workflows, documentation quality, stakeholder experience, and program reporting.
You’llneed to have:
- 6\+ years of experience in technology risk, data risk, security risk, AI/ML, model risk, privacy, compliance, governance, or product risk.
- 2\+ years of experience focused on AI governance, model governance, responsible AI, AI/ML risk, technology risk, data governance, or related areas.
- Working understanding of AI/ML and generative AI risks, including bias, explainability, model drift, robustness, hallucination, privacy, security, and data quality.
- Familiarity with generative AI tools and platforms.
- Experience supporting risk assessments, control reviews, policy implementation, issue tracking, or governance processes.
- Strong analytical and problem\-solving skills, with the ability to assess risk in practical business and technical contexts.
- Strong communication skills, including the ability to write clearly and work effectively with technical and non\-technical stakeholders.
- Ability to manage multiple reviews, priorities, and stakeholders in a fast\-moving environment.
We’dlove to see:
- Experience working with AI/ML development teams, data science teams, engineering teams, or product teams.
- Familiarity with NIST AI RMF, ISO/IEC 23894, EU AI Act, OECD AI Principles, GDPR, CPRA, or similar frameworks.
- Experience with model inventories, AI inventories, model documentation, risk management platforms, GRC tools, MLOps, LLMOps, or AI monitoring tools.
- Experience supporting third\-party technology risk, vendor reviews, or AI\-enabled vendor assessments.
- Certifications in risk, privacy, security, compliance, or AI governance.
- Curiosity about AI and a practical mindset for helping teams innovate responsibly.
Salary Range \= 130,000 \- 180,000 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 $130K-$180K range is below the median 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. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $130K to $180K.
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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