Interested in this AI/ML Engineer role at Playlist?
Apply Now →About This Role
### About the Company:
At Playlist, life's richest moments happen when people step away from screens to move, connect, explore, and play. We're building the definitive platform for intentional living, connecting people with inspiring experiences in fitness, wellness, and beyond. With popular brands like Mindbody and ClassPass, Playlist empowers businesses and individuals, making it effortless for aspirations to become actions. Join us in reshaping technology's role to foster meaningful, real\-world connections.
About the Team
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We're building new, AI\-native vertical software that extends Playlist's mission to empower wellness businesses across more of the health and wellness market. We are looking for entrepreneurial, high\-ownership design leaders who can drive the full product lifecycle of 01 solutions for SMB operators, defining their operating tools and workflows, ensuring seamless onboarding and adoption, while building a long\-term roadmap that grows with them.
As the founding designer for new verticals, you'll work as part of a small, fast\-moving founding team (engineering, product, and ops). You'll be expected to go into the field to learn alongside wellness business operators, designing and iterating on product requirements, validating concepts, and leveraging AI to both accelerate how we build and drive genuinely new customer experiences.
This is a senior individual contributor role focused on defining and launching new products from the ground up, identifying unmet customer needs, crafting product vision, and partnering closely with Product and Engineering to bring new platform businesses to life.
If you love ambiguity, have thrive in 01 environments, and enjoy turning complex business problems into elegant product experiences, we'd love to talk.
The Role You'll Play
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- Own, evolve, and champion the product vision, strategy, and roadmap for new vertical SMB products, aligned with our broader company vision and product ecosystem.
- Drive our AI\-native design philosophy: where AI replaces workflow, where it consults, and where it automates on behalf of the customer. Software that works while they're with a client.
- Get into the field. Build direct relationships with practitioners, understand their unmet needs, and iterate live on what you learn
- Work directly with a small engineering and product team to ship fast; own the roadmap, the spec, and the release.
- Bring high agency and creativity to every stage of the build, from blank page to a product customers can't imagine running their business without
- Create compelling end\-to\-end product experiences that connect customer needs, business goals, and technical capabilities.
- Build and leverage LLM\-powered tools (and create them when they don't exist)
- Identify opportunities where AI, automation, and intelligent systems can create differentiated customer value.
- Influence executive stakeholders through storytelling, customer insights, strategic thinking, and design excellence.
The Experience You Bring
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- 10\+ years of design experience, with experience building tools that businesses depend on day\-to\-day
- You've shipped 01 products and successfully scaled the customer base. You can walk through what broke, what worked, and what you'd do differently
- You've built with AI or built AI\-native products. You have real opinions on where LLMs break, how to design for probabilistic outputs, and how to earn user trust in an AI\-driven experience
- You're comfortable with ambiguity and high agency. You move without being told, define your own priorities, and thrive when the roadmap is yours to create
- You are energized by shaping product strategy, not just executing requirements.
- Bonus: experience in health, wellness, fitness, or service\-business SMB. You understand the texture of running a small practice or studio
- Ability to move fluidly between strategic vision work and hands\-on design execution.
- Strong systems thinker who can design across workflows, ecosystems, and interconnected products.
You'll Thrive Here If You
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- Are energized by building what doesn't exist yet.
- Enjoy solving difficult customer and business problems.
- Think in systems, not screens.
- Can navigate ambiguity without waiting for direction.
- Love collaborating with smart, opinionated partners across disciplines.
- Balance vision with execution.
- Care deeply about craft while staying focused on outcomes.
### Have we piqued your curiosity?
Sound like the role for you? We'd love to hear from you! Even if you're not 100% sure about potential fit, we still encourage you to apply. We're looking for the right person, not the perfect series of checkboxes.
The Company is an Equal Opportunity Employer. We highly value diversity at our company and encourage people of all different backgrounds, experiences, abilities and perspectives to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected characteristics.
*By entering your email and phone number and submitting your application, you consent to receive emails, calls and SMS about your application and other roles at The Company, including by auto\-dialer. Message and data rates may apply. Opt\-out or text STOP to cancel at any time. If you are a California resident or reside outside the United States then by submitting your application you confirm that you have read, understood, agree and \- where applicable \- grant your prior, free, informed and express consent for the processing of your personal information, including sensitive personal information, as described in our* *California Applicant Privacy Notice* *or* *International Applicant Privacy Notice* *(as applicable).*
*Note: This description outlines key responsibilities but isn't intended to cover every task or duty. Additional responsibilities may be assigned as needed to support the team and business goals.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Playlist, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Playlist AI Hiring
Playlist has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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