Interested in this AI/ML Engineer role at SiriusXM?
Apply Now →Skills & Technologies
About This Role
Responsibilities:
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 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:
We are seeking a Director, Marketing Operations to lead the development and execution of high\-quality marketing campaigns and customer journeys that drive engagement, conversion, and revenue. This role is responsible for translating marketing strategy into accurate, scalable, and measurable campaign execution that delivers real business impact.
You will lead a team focused on building, testing, and launching multi\-channel campaigns and journeys, partnering closely with Marketing, Ops, and Analytics to ensure alignment on strategy, segmentation, and measurement. You will establish standards and best practices for campaign and journey development, ensuring quality, accuracy, and scalability as marketing programs grow in complexity. This role plays a key part in shaping how customers engage with SiriusXM and how marketing drives conversion and revenue.
What you’ll do:
- Oversee and evolve the design, build, and execution of high\-quality, multi\-channel campaigns and customer journeys that drive engagement, conversion, and revenue.
- Translate marketing strategy into scalable, accurate, and measurable campaign execution that delivers business outcomes.
- Establish and enforce standards, best practices, and governance for campaign and journey development, ensuring quality, accuracy, and scalability.
- Drive campaign performance and data insights to identify opportunities for improvement, enhanced targeting, and increased revenue impact.
- Oversee the operational readiness and transition of campaigns, including migration efforts, ensuring continuity and accuracy.
- Facilitate cross\-functional collaboration to streamline processes, remove operational bottles, and scale marketing programs efficiently.
- Provide leadership, coaching, and development for the team, fostering accountability innovation, and operational excellence,
- Serve as a strategic partner to business stakeholders, ensuring marketing operations enables campaigns that deliver measurable growth and customer impact.
- Leadership \& Team Development
+ Lead, coach, and develop high\-performing teams to foster career growth and professional development.
+ Provide constructive feedback, conduct performance reviews, and guide team members in setting and achieving goals.
+ Build and maintain an inclusive, collaborative team culture that encourages innovation and accountability.
What you’ll need:
- 10\+ years of experience working with enterprise data and marketing platforms (e.g., Teradata, Aprimo, Unica, etc.), including 5\+ years hands\-on experience within the Salesforce ecosystem, with deep expertise in Marketing Cloud and exposure to Data Cloud.
- 5\+ years of people leadership experience, with a history of building, coaching, and scaling high\-performing marketing operations and campaign teams in fast\-paced environments.
- Proven hands\-on campaign builder with the ability to design, execute, and optimize complex, omni\-channel campaigns across email, mobile, paid media, and integrated digital channels leveraging Salesforce Marketing Cloud.
- Expert\-level, hands\-on experience with Salesforce Marketing Cloud architecture, including Contact Builder data modeling, attribute groups, sendable data extensions, SQL automations, REST/SOAP integrations, and Journey Builder Orchestration.
- Demonstrated ability to architect scalable campaign frameworks, including journeys, automations, data models, and audience segmentation strategies that support personalization, experimentation, and growth.
- Strong track record of teaching, mentoring, and up\-leveling teams, enabling marketers and operators to independently build and manage sophisticated Salesforce campaigns while maintaining governance and best practices.
- Advanced understanding of database marketing concepts, including data modeling, audience strategy, identity resolution, and campaign measurement.
- Deep technical proficiency in SQL and experience partnering closely with data engineering and analytics teams to translate business needs into actionable campaign logic.
- Experience establishing campaign standards, documentation, QA processes, and operational workflows that balance speed, quality, and compliance.
- Experience with automation and/or AI tools such as chatGPT, Gemini, etc.
- Bachelor’s degree or equivalent in Business, Statistics, Mathematics, Engineering, or a related analytical field.
- 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 $160,000 \- $180,000 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.
Salary Context
This $160K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At SiriusXM, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
SiriusXM AI Hiring
SiriusXM has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.