Member of Technical Staff – Applied ML, RecSys
Liquid AI
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • United States
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Job Level
About the role
- Act as the technical owner for enterprise customer engagements involving recommendation and ranking workloads
- Translate customer requirements into concrete specifications for recommendation models
- Design and execute data pipelines for user interaction data, feature engineering, and training data curation at scale
- Fine-tune and adapt large-scale sequential recommendation models (e.g., HSTU-style architectures) for customer-specific use cases
- Design task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret results
- Build reusable applied tooling and workflows that accelerate future customer engagements
Requirements
- Hands-on experience building or fine-tuning recommendation models at scale (not just off-the-shelf collaborative filtering)
- Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems
- Strong intuition for data quality and evaluation design in recommendation contexts (offline metrics, A/B testing, business metric alignment)
- Experience with large-scale data pipelines for user interaction data and feature engineering
- Proficiency in Python and PyTorch with autonomous coding and debugging ability
Benefits
- We pay 100% of medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO plus company-wide Refill Days throughout the year
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
recommendation modelssequential recommendation architecturesuser behavior modelinglarge-scale ranking systemsdata pipelinesfeature engineeringPythonPyTorchA/B testingevaluation design
Soft Skills
technical ownershipcustomer engagementintuition for data qualityinterpretation of resultsdesigning evaluationsbuilding reusable toolingworkflow acceleration