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Machine Learning Engineer

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Acceler8 Talent
Full Time position
Listed on 2026-03-15
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 125000 - 150000 USD Yearly USD 125000.00 150000.00 YEAR
Job Description & How to Apply Below

📍
Talent Marketplace - onsite 5 days a week, San Francisco.

I’m working with a fast-growing talent marketplace building infrastructure that helps companies fill critical roles faster by matching them with specialist recruiters.

Instead of fragmented agency workflows, the platform centralizes the process and compounds network intelligence with every search, submission, and interview.

They’ve seen serious momentum
: 🚀

  • Rapid revenue growth since their seed round
  • Used by leading AI and high-growth technology companies
  • Thousands of successful placements through the platform
  • Marketplace generating significant earnings for recruiters
The Role

This is a AI/ML role focused on recommendation and ranking systems
.

Not research.

You’ll be building models that directly improve matching quality and marketplace outcomes in production
.

Typical problems may include:

  • Ranking and recommendations
  • Improving match quality across a two-sided marketplace
  • Understanding supply / demand dynamics and marketplace incentives

This will be one of the early ML hires
, so there’s real ownership and influence on how the ML function evolves.

What They’re Looking For
  • Experience shipping recommendation or ranking systems in production
  • Strong applied ML fundamentals with a product mindset
  • Experience tying model performance to product and business metrics
  • Comfort operating in fast-moving startup environments
Why This Role Is Interesting

You’ll be working on ML problems where model quality directly affects real outcomes - who gets hired, how quickly roles are filled, and how efficiently a marketplace operates.

It’s a chance to own recommender systems early inside a rapidly scaling product.

If you’ve built ranking or recommender systems in production, I’d love to chat.

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