Interested in this AI/ML Engineer role at Spotify?
Apply Now →About This Role
Engineering
Engineering Leadership
Permanent
New York As the Engineering Manager of the Oasis Squad, you’ll lead a multidisciplinary team of machine learning, data, and backend engineers building ML systems at the heart of how Spotify shapes and delivers music experiences.
Oasis needs a people leader with significant technical machine learning depth who can help the team navigate complex technical strategy, shifting business needs, and high-impact cross-functional decisions. A great manager for this team will give experienced engineers meaningful autonomy while staying deeply engaged in their technical work. You’ll have the ML expertise to assess technical tradeoffs, steer the team toward decisions, and represent its technical direction with senior partners across Personalization, Product, and the music business. This is a role for someone who enjoys being close to both the people and the work: coaching engineers, creating clarity amidst constant change, and enabling a high-performing team to move quickly without adding unnecessary process.
If this role excites you but you don't meet every requirement, we'd still love to hear from you. We welcome candidates from all backgrounds.
What You'll Do
- + Lead, coach, and develop a multidisciplinary team of ML, data, and backend engineers, giving experienced ICs meaningful autonomy while providing active mentorship and coaching where needed. + Provide technical leadership on complex ML systems, engaging deeply enough with modeling strategy and implementation to challenge assumptions, assess tradeoffs, identify risks, and help the team make decisions. + Represent and advocate for the team with cross-functional stakeholders across business, product, insights, and the personalization mission, balancing technical concerns with business needs. + Empower engineers to own technical decisions, but recognize when the team needs you to provide direction, resolve a tradeoff, or make the decision yourself. + Be the point of contact for requests from cross-functional partners, filtering the important updates to the team and preserving their heads down time. + Own healthy delivery, including prioritization, capacity management, operational health, and reliable execution, while favoring lightweight processes that help the team move quickly and learn. + Stay close to the engineering craft and act as a player-coach when useful, including contributing hands-on to technical problem solving and engineering work. + Help the team use AI effectively in engineering workflows and identify opportunities for AI to improve both how we build and the ML systems we create. + Hire and onboard engineers as the team evolves, building the capabilities and knowledge distribution needed for the team to remain resilient over time.
Who You Are
- + Experience managing engineers and a demonstrated track record of developing and managing technical ICs. + You have a solid background in machine learning and can engage credibly in modeling strategy, ML system design, and technical discussions with experienced ML practitioners. + You can independently assess ML strategies and technical tradeoffs, challenge assumptions, and clearly represent a technical direction with both technical and non-technical stakeholders. + You are comfortable operating in an environment where technical strategy must account for evolving product and business needs, and can create clarity for your team through sudden shifts in direction. + You build trust with experienced engineers by giving them autonomy and avoiding unnecessary oversight, while remaining close enough to their work to coach effectively, understand risks, and step in when needed. + You view yourself as your team’s advocate. + You are a thoughtful leader who knows when to facilitate a decision, when to let the team decide, and when to make the decision yourself. + You favor lightweight, purposeful processes over process for its own sake. You can maintain a strong understanding of work, risks, and delivery without creating unnecessary overhead for the team. + You are comfortable making calculated tradeoffs, learning through iteration, and helping teams move quickly while maintaining appropriate technical and operational standards. + You can manage substantial technical, product, and business dependencies with partner teams and build credibility with senior cross-functional stakeholders. + You are comfortable using AI tools and have a point of view on how AI can improve engineering effectiveness and evolve ML-powered products. + You value healthy disagreement, clear ownership, accountability, and an inclusive environment where people can do their best work. + Experience with data engineering and backend is a plus.
Where You'll Be
- + We offer you the flexibility to work where you work best! For this role, it can be within the North America region in which we have a work location. + This team collaborates across the Eastern time zone
Learn about life at Spotify
The United States base range for this position is $184,049 - 262,928 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens. At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Our global benefits
Extensive learning opportunities, through our dedicated team, GreenHouse. Flexible share incentives letting you choose how you share in our success. Global parental leave, six months off - for all new parents. All The Feels, our employee assistance program and self-care hub. Flexible public holidays, swap days off according to your values and beliefs.
Salary Context
This $184K-$262K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Spotify, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($223K) sits 5% above the category median. Disclosed range: $184K to $262K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Spotify AI Hiring
Spotify has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, New York, NY, US. Compensation range: $262K - $262K.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.