Spotify
Senior Staff Machine Learning Engineer - Agentic Systems
New York, New York, USA · Staff+ · full-time
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · New York-Newark-Jersey City, NY-NJ. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
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About the role
- These ranges may be modified in the future.
- Spotify is an equal opportunity employer.
Responsibilities
- We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses.
- Spotify's Personalization team is building the next generation of intelligent listening experiences.
- Generative AI is reshaping how we build products and systems.
Requirements
- 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.
Compensation
- $281k-$402k
Benefits
- 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, paid sick leave.
Company info
- As part of this shift, we're creating a shared Agent Engine that powers agent-based experiences across Spotify.
- At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone.
- Our platform is for everyone, and so is our workplace.
- As part of this
Equal opportunity
- equal opportunity employer.
This listing is sourced directly from Spotify's careers page and normalized into a canonical job model.