Machine Learning Engineer, Marketplace
Build ML models and decision systems for search, ranking, candidate-job matching, and marketplace optimization at a fast-growing AI talent platform.
About the job
What You’ll Build
- Ranking and matching systems that determine which candidates and opportunities are surfaced
- Models for recommendation, personalization, and marketplace optimization
- Retrieval, scoring, and decision pipelines operating at global scale
- Feedback loops that learn from downstream hiring outcomes, not just top-of-funnel engagement
- Real-time and batch inference systems embedded in product-critical workflows
Example Problems
- Improve candidate-job matching using embeddings, structured attributes, and behavioral signals
- Optimize ranking toward long-term hiring outcomes under delayed and incomplete labels
- Design models that balance marketplace objectives such as fill rate, quality, speed, and conversion
- Build systems for candidate allocation, opportunity routing, and liquidity optimization
- Develop evaluation and experimentation frameworks that connect model performance to business results
What We’re Looking For
- Strong track record of shipping ML systems into production
- Experience with ranking, recommendation, search, matching, or marketplace problems
- Good judgment on model design, objective functions, evaluation, and tradeoffs
- Comfort working across the full applied ML stack: data, features, training, inference, and iteration
- Strong engineering fundamentals and a bias toward simple, robust systems
Tech Stack
Python, Go, embeddings, fine-tuning, RAG, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform
Benefits
- Bi-annual performance bonus structure
- Generous equity grant vested over 4 years
- Up to $15k Relocation bonus
- $10K housing bonus (if you live within 0.5 miles of our office)
- $1.5K monthly stipend for meals
- Free Equinox membership
- $200 monthly laundry reimbursement
- $200 monthly personal wellness reimbursement
- Health, Dental, Vision insurance
Skills
Python, Go, Machine Learning, Embeddings, Fine-Tuning, RAG, Ranking, Recommendation Systems, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform
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