Job brief: Senior Machine Learning Engineer (Growth)

What JoRoute checked · Checked by JoRoute

• Listed on Discord's own job board. JoRoute does not take listings from job ads or recruiters.
• First seen by JoRoute 2026-11-04 (1 day ago); last confirmed on the board 2026-11-05.
• The apply link opens job-boards.greenhouse.io, the address Discord's board gives for this job.
• No other listing with this title and place on this board in the last 90 days.
• No layoff notices (WARN) under this name in the state files JoRoute reads.

What the listing says · Employer's own words, not checked

• Pay: $220,000 to $275,000 a year
• Job type: Not stated
• Where: San Francisco Bay Area · San Francisco, CA
• Posted: 2026-11-04

What the job asks · Employer's own words, not checked

Level: Senior. The title says "Senior".

Must have

• 5+ years of experience in Machine Learning, Data Science, or Applied Science, with a track record of shipping recommendation or personalization systems end to end.

Main duties

• Design, build, and scale the recommendation systems that connect new users to communities and content, from candidate generation and ranking through to onlin…
• Build and deploy ML models that lift new-user retention, combining purpose-built approaches with shared foundations such as user and server embeddings.
• Collaborate cross-functionally to shape the Onboarding roadmap, balancing fast learning experiments with the long-term complexity and scalability of the syst…

Lines copied from the posting, not rewritten. "Preferred" or "nice to have" items are not listed as must-haves.

How it compares · Compared by JoRoute from listings

• Typical listed pay for marketing jobs in San Francisco, CA: $198k a year (119 listings, 19 employers). This listing is 25% higher.
• Across Discord's 27 open listings, 56% of those with a posting date were first posted more than 60 days ago. How Discord hires

What nobody outside the employer knows · Not known

• Whether the role is already filled or on hold: employers often leave listings up.
• How many people applied, and who reads the applications.
• Where in the published range an offer would land.

Ask before you apply

1. Which of the listed places is this role based in?
2. What are the interview steps, and when should I expect to hear back?

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Next steps

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