Interested in this AI/ML Engineer role at adswizz?
Apply Now →About This Role
- New York, New York
- Regular Employee Full-Time
- R-2026-09-27
- Hybrid
Who We Are:
SiriusXM and its brands (Pandora, SiriusXM Media, AdsWizz, Simplecast, and SiriusXM Connect) are leading a new era of audio entertainment and services by delivering the most compelling subscription and ad-supported audio entertainment experience for listeners - in the car, at home, and anywhere on the go with connected devices. Our vision is to shape the future of audio, where everyone can be effortlessly connected to the voices, stories and music they love wherever they are.
This is the place where a diverse group of emerging talent and legends alike come to share authentic and purposeful songs, stories, sounds and insights through some of the best programming and technology in the world. Our critically-acclaimed, industry-leading audio entertainment encompasses music, sports, comedy, news, talk, live events, and podcasting. No matter their individual role, each of our employees plays a vital part in bringing SiriusXM’s vision to life every day.
SiriusXM
SiriusXM is the leading audio entertainment company in North America, and the premier programmer and platform for subscription and digital advertising-supported audio products. SiriusXM’s platforms collectively reach approximately 150 million listeners, the largest digital audio audience across paid and free tiers in North America, and deliver music, sports, talk, news, comedy, entertainment and podcasts. Pandora, a subsidiary of SiriusXM, is the largest ad-supported audio entertainment streaming service in the U.S. SiriusXM's subsidiaries Simplecast and AdsWizz make it a leader in podcast hosting, production, distribution, analytics and monetization. The Company’s advertising sales organization, which operates as SiriusXM Media, leverages its scale, cross-platform sales organization and ad tech capabilities to deliver results for audio creators and advertisers. SiriusXM, through SiriusXM Canada Holdings, Inc., also offers satellite radio and audio entertainment in Canada. In addition to its audio entertainment businesses, SiriusXM offers connected vehicle services to automakers.
How you’ll make an impact:
As the Senior Vice President, Data & AI Enablement, you will report directly to the Chief Product & Technology Officer and lead SiriusXM’s enterprise Data & AI strategy, shared foundation, governance, enablement capabilities, and measurable business impact.
You will bring together the capabilities needed to make data trusted, reusable, accessible, and actionable across the company, including Data Platform & Governance, Applied ML and Experimentation, Enterprise AI Standards, Agent and LLM Platforms, and Responsible AI and Adoption. You will establish common platforms, standards, controls, and talent practices while enabling domain teams to retain ownership of the business and product outcomes they support.
You will partner closely with the Chief Product & Technology Officer, Chief Financial Officer, Executive Leadership Team, and leaders across the business to align Data & AI priorities, investments, operating models, and value realization across SiriusXM.
What you’ll do:
Enterprise Data & AI Strategy
- Design and build the Data & AI Enablement organization, including its leadership structure, capability model, federated partnerships, decision rights, and operating model.
- Partner with the Chief Financial Officer and other Finance leaders to shape enterprise data strategy, investment choices, financial and operating measurement, and value realization.
Data Platform, Architecture & Enterprise Data Products
- Own SiriusXM’s shared data and ML foundation, including core platform capabilities across storage, ingestion, streaming, orchestration, modeling, APIs, BI, and ML workflows.
- Establish the architecture, standards, and operating practices required to ensure shared data platforms are secure, reliable, observable, cost-efficient, and scalable, while retiring redundant legacy technologies.
- Partner with the CTO, CIO, Architecture, Security, Privacy, and domain engineering leaders to align platform roadmaps, technical capacity, enterprise architecture, and key operating dependencies.
- Manage shared data capabilities as enterprise products with clear ownership, roadmaps, service expectations, adoption measures, lifecycle management, and retirement plans.
- Build reusable data products and self-service capabilities, including enterprise metrics and semantic definitions, identity frameworks, experimentation and measurement infrastructure, curated datasets, APIs, cataloging, and enablement.
- Establish a governed enterprise framework for high-value metrics and data definitions, with clear ownership, business rules, calculation logic, versioning, and approved use cases in partnership with Finance and business leaders.
Data Governance, Quality & Responsible AI
- Own SiriusXM’s enterprise framework for data governance, quality, privacy, access, retention, permitted use, and control automation in partnership with Privacy, Legal, Security, and domain leaders.
- Establish clear accountability for critical data, enterprise definitions, quality standards, monitoring, issue resolution, and escalation, with governance embedded through scalable standards and automation.
- Lead enterprise AI standards and responsible AI practices, including approved tooling, risk frameworks, data-use expectations, human oversight, model monitoring, auditability, and post-launch measurement.
- Integrate data and AI governance into a coherent enterprise operating model that enables innovation while preserving clear decision rights, regulatory compliance, and security.
- Drive responsible AI adoption across SiriusXM by translating governance into practical, scalable operating practices that support secure innovation, measurable impact, and human-centered use of AI.
Data Science, Applied ML & Enterprise AI Enablement
- Establish the enterprise Data Science and Applied ML discipline, including standards for model development, experimentation, evaluation, MLOps, production adoption, performance management, and talent development.
- Lead Applied ML and Experimentation in partnership with Product, Engineering, Marketing, Advertising, Automotive, and business leaders to move high-value use cases from concept to measurable production impact.
- Enable product-embedded ML teams through shared platforms, reusable capabilities, governance, evaluation frameworks, and communities of practice, while preserving accountability within Product and Engineering.
- Build and scale SiriusXM’s Agent and LLM platform and enterprise AI enablement model, including approved tools, reusable patterns, training, adoption mechanisms, and ongoing support.
- Drive AI-enabled ways of working across Product & Technology and the broader enterprise, with clear measures of adoption, productivity, quality, speed, and organizational capacity.
- Partner with People and functional leaders to evolve roles, skills, and operating models so AI transformation strengthens organizational leverage, workforce readiness, and appropriate governance.
Business, Product & Domain Partnerships
- Build durable partnerships across SiriusXM’s major product and business domains, including listener experiences, personalization and discovery, automotive, advertising and MarTech, subscriptions and growth, content and programming, and enterprise operations.
- Enable domain analytics and applied science teams to translate business priorities into actionable insights, experimentation, recommendations, and production capabilities that drive measurable outcomes.
- Establish a clear federated operating model in which domain and product leaders retain accountability for business outcomes and priorities, while Data & AI Enablement provides shared capabilities, standards, governance, talent practices, and enterprise-wide coordination.
Investment, Measurement & Executive Accountability
- Partner with the CPTO, CFO, and Finance leadership to align Data & AI priorities, investment decisions, enterprise metrics, platform economics, and value realization with broader business and technology priorities.
- Establish an enterprise Data & AI scorecard that measures business impact, data trust, platform performance, adoption, risk and control health, workforce effectiveness, and realized value.
- Define clear executive measures of effectiveness, including speed from approved business need to trusted decision or production capability, while distinguishing realized benefits from forecasts and surfacing material risks and variances.
- Provide regular executive reporting on progress, investment performance, trade-offs, dependencies, risks, decisions, and measurable outcomes.
Organization, Leadership & Talent
- Establish a strong functional home for Data & AI talent, with clear career frameworks, leadership expectations, talent development, and succession planning across critical disciplines.
- Build the leadership capacity and organizational mechanisms required to manage a broad, enterprise-wide mandate with clarity, accountability, and effective decision-making.
What you’ll need:
- Typically has 20 or more years of progressive leadership experience across enterprise data, analytics, artificial intelligence, machine learning, data platforms, technology, or a comparable complex technology-enabled environment.
- 10–15 years of progressive leadership experience, including significant experience leading senior teams and complex organizations.
- Bachelor’s degree in computer science, engineering, mathematics, statistics, business, or a related field; advanced degree is a plus.
- Significant executive leadership experience across data, analytics, AI, technology, or enterprise enablement, with accountability for strategy, execution, talent, investment, and measurable business outcomes.
- Deep experience shaping enterprise data strategy and modernizing shared platforms, governance, data quality, and self-service capabilities.
- Proven success leading federated operating models that balance enterprise standards, platforms, controls, and talent practices with domain-level ownership and execution.
- Strong expertise across modern Data & AI capabilities, including applied ML, experimentation, MLOps, generative AI and LLM platforms, responsible AI, and enterprise adoption.
- Strong understanding of data architecture, identity, semantic models, metadata and lineage, privacy, security, compliance, and operational reliability.
- Track record of translating Data & AI investments into measurable customer, financial, and operating outcomes, including growth, retention, engagement, personalization, efficiency, and improved decision-making.
- Demonstrated ability to partner with senior executives across Product, Technology, Finance, and the business to set priorities, evaluate investments, resolve trade-offs, and drive enterprise outcomes.
- Experience leading large, complex organizations through transformation, operating-model change, and organizational scaling.
- Exceptional executive presence and communication skills, with the ability to translate complex technical and analytical topics into clear decisions and action.
- A pragmatic, enterprise-minded leader who knows what to centralize, what to federate, and how to create leverage without unnecessary bureaucracy.
- A strong people leader who develops senior talent, builds organizational capability, and fosters a culture of accountability, innovation, inclusion, and continuous improvement.
- Deep commitment to responsible AI, data stewardship, privacy, security, and the trust required to scale Data & AI across the enterprise.
- Experience in media, advertising, subscriptions, consumer technology, automotive, connected services, or another complex multi-domain business preferred.
- Must have legal right to work in the U.S.
At SiriusXM, we carefully consider a wide range of factors when determining compensation, including your background and experience. These considerations can cause your compensation to vary. We expect the base salary for this position to be in the range of $342,000 to $416,000 and will depend on your skills, qualifications, and experience. Additionally, this role might be eligible for discretionary short-term and long-term incentives. We encourage all interested candidates to apply.
Our goal at SiriusXM is to provide and maintain a work environment that fosters mutual respect, professionalism and cooperation. SiriusXM is an equal opportunity employer that does not discriminate on the basis of actual or perceived race, creed, color, religion, national origin, ancestry, alienage or citizenship status, age, disability or handicap, sex, gender identity, marital status, familial status, veteran status, sexual orientation or any other characteristic protected by applicable federal, state or local laws.
The requirements and duties described above may be modified or waived by the Company in its sole discretion without notice.
R-2026-09-27
Salary Context
This $342K-$416K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At adswizz, 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 $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($379K) sits 79% above the category median. Disclosed range: $342K to $416K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
adswizz AI Hiring
adswizz has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $416K - $416K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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