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Staff Machine Learning Engineer, Recommendations
$205K–$265K
Remote, Santa Monica, CA +2 · Remote · Staff · Full-time · Posted Sep 3, 2026
- Location
- Remote, Santa Monica, CA
Remote
Santa Monica, CA, United States - Experience
- 8+ years of professional experience developing and deploying machine learning models in large-scale production environments.
- Education
- Advanced degree (MS/PhD) in Computer Science, Machine Learning or a related quantitative field or equivalent experience.
About the job
Leads the design, deployment, and improvement of large-scale machine learning systems for job recommendations, matching, ranking, and retrieval.
What you'll do
- Shape the ML roadmap and architecture for recommendation and matching systems in partnership with Engineering and Product leadership.
- Build and own high-throughput production systems for interaction prediction, candidate ranking, and job/candidate retrieval, including end-to-end model evaluation and latency optimization.
- Mentor ML engineers and data scientists and promote technical excellence, experimentation, and production delivery.
What they ask for
- Proven ability to architect and ship end-to-end ML solutions serving production traffic at scale.
- Deep expertise in recommendation systems, personalization, ranking and retrieval, or interaction prediction.
- Strong software engineering fundamentals and hands-on experience with PyTorch and TensorFlow.
- Experience in technical leadership and mentorship, statistical modeling, online experimentation, and offline metric design.
Nice to have
- Two-sided marketplace experience or familiarity with liquidity, bilateral matching, dynamic pricing, or auction-based models.
- Experience with Two-Tower Neural Networks, Graph Neural Networks (GNNs), Transformer-based retrieval models, Contextual Bandits, MLOps, or distributed training.
Pay
$205,000.00-$265,000.00 USD base salary
Tools
PyTorch, TensorFlow, Two-Tower Neural Networks, Graph Neural Networks (GNNs), Transformer-based retrieval models, Contextual Bandits
AI focus
Develops and deploys AI/ML for recommendations, personalization, matching, ranking, retrieval, and real-time intent prediction.
Model-written summary · the original posting is authoritative