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About Suno
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We're building the world's first creative entertainment platform, where the entire world can feel the joy and fulfilment of making music. Music is for everyone: Our users include everyone from grandmothers creating songs for their loved ones, to Grammy winners using Suno Studio, our power tool, to make the most popular hits in the world.
Building the future of entertainment requires ambition. The pace is fast, the problems are hard, and the work demands ownership and intensity. For the right people, it’s incredibly rewarding: a chance to shape a new medium, work with a small team that cares deeply about quality, make music, drink too much coffee, and build something that millions of people use to express themselves in ways that were never before possible.
Suno is the fastest growing consumer entertainment company and the leader in AI music. We are backed by leading investors including Bond Capital, Menlo Ventures, Lightspeed Venture Partners, IVP, Forerunner, Union Square Ventures, Alkeon, Quiet, Matrix Partners, Schroders Capital and, NVentures (venture arm of NVIDIA).
Founded in 2023 by Harvard alumni with a shared passion for music, Suno has quickly grown to 200 employees while empowering more than 100 million people to create original songs. In 2026, we raised a $400 million Series D at a $5\.4 billion valuation to continue building innovative products that amplify creativity and imagination.
About the Role
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Artificial intelligence is transforming every creative field. At Suno, we are building one of the world's leading generative music models, enabling millions of people to become musicians for the first time.
We are hiring the first Product Manager dedicated to our Research organization.
In this role, you will partner closely with world\-class machine learning engineers to identify the highest\-impact opportunities for improving our models. By combining user understanding with rigorous evaluation and product judgment, you will help researchers focus on the problems whose solutions have the greatest impact on our business.
This is a rare opportunity to bring product judgment into the model development process: helping ensure our research efforts are grounded in real user needs, evaluated thoughtfully, and translated into meaningful improvements in what people can create with Suno.
Listen to the song we made about it: https://suno.com/s/Y0WfEP7qALvaZanH
What You'll Do
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- Define what “good” means — Own the evaluation frameworks and quality metrics for Suno’s foundational music model: what we measure, why it matters, and how we know the model is improving.
- Connect creator needs to model capabilities — Deeply understand how people create with Suno, identify where the model helps or falls short, and translate those insights into research problems the team can act on.
- Bridge product and research — Partner with user\-facing PMs, engineers, and data scientists to understand real\-world model behavior and turn those learnings into better evaluations, clearer priorities, and more useful capabilities.
- Shape model priorities in partnership with Research — Bring user insight, data, and business context into roadmap discussions, helping the team make thoughtful tradeoffs when research opportunities and product needs compete.
- Bring new models and capabilities to market — Work with Research, Product, and Product Marketing to launch model improvements in a way users can understand, adopt, and get value from.
- Go deep technically — Run experiments, dig into model behavior, and work in the details alongside researchers and data scientists.
Who You Are
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A product leader with a deep passion and curiosity for AI:
- 6\+ years of product management experience, or equivalent experience building highly technical products
- Experience partnering closely with machine learning researchers, model training teams, or AI evaluation systems
- Strong technical intuition and the ability to engage deeply with complex machine learning concepts
- A strong technical foundation, ideally with a background in computer science, engineering, mathematics, physics, or a related field
- Exceptional product judgment and the ability to bring clarity and structure to ambiguous, research\-driven problems
- A first\-principles thinker who is naturally curious about how frontier AI models work and how they can be improved
- Operates with urgency and thrives in fast\-moving, iterative environments
- Excited by the intersection of AI, creativity, and music
Bonus Points
- Experience working at a frontier AI lab or on a foundation model team
- Experience designing or building AI evaluation frameworks, benchmarks, or human evaluation systems
- Experience working with large\-scale datasets, data quality, or annotation pipeline.
Additional Notes
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- Applicants must be eligible to work in the US.
- This role is expected to work from the designated Suno office 5 days a week, per Suno's company policy.
- Must be willing to travel once per quarter for larger team meetings.
Perks \& Benefits for Full\-Time Employees
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- Company Equity Package
- 401(k) with 3% Employer Match \& Roth 401(k)
- Medical, Dental, \& Vision Insurance (PPO w/ HSA \& FSA options)
- 11 Paid Holidays \+ Unlimited PTO \& Sick Time
- 16 Weeks of Paid Parental Leave
- Creative Education Stipend
- Generous Commuter Allowance
- In\-Office Lunch (5 days per week)
Compensation:
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$250K – $330K base salary (before equity)
Suno is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the Massachusetts Fair Chance in Employment Act, NYC Fair Chance Act, LA City Fair Chance Ordinance, and San Francisco Fair Chance Ordinance.
Compensation Range: $250K \- $330K
Salary Context
This $250K-$330K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At suno, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($290K) sits 33% above the category median. Disclosed range: $250K to $330K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
suno AI Hiring
suno has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $265K - $360K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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