5 months ago
Responsibilities
- Own enterprise customer engagements involving recommendation and ranking workloads as the technical owner.
- Translate customer requirements into concrete recommendation-model specifications.
- Design and execute data pipelines, feature engineering, and training-data curation for user interaction data at scale.
- Fine-tune and adapt large-scale sequential recommendation models for customer-specific use cases.
- Design task-specific evaluations for ranking quality, latency, and throughput and interpret the results.
- Build reusable applied tooling and workflows to accelerate future customer engagements.
- Balance model quality, latency, and business impact while communicating technical decisions to customers.
Requirements
- Hands-on experience building or fine-tuning recommendation models at scale.
- Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems.
- Strong understanding of data quality and evaluation design for recommendations, including offline metrics, A/B testing, and business-metric alignment.
- Experience building large-scale data pipelines for user interaction data and feature engineering.
- Proficiency in Python and PyTorch with autonomous coding and debugging ability.
- Experience with transformer-based recommendation architectures such as HSTU, SASRec, or BERT4Rec is preferred.
- Experience delivering recommendation systems to external customers with measurable business outcomes is preferred.
- Familiarity with serving recommendation models under latency and throughput constraints is preferred.
Benefits
- 100% employer-paid medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited paid time off
- Company-wide Refill Days throughout the year
- Competitive base salary with equity
- Enterprise customer engagements involving real production recommendation systems
Categories
Data EngineeringML Engineering
About Liquid AI
We build efficient general-purpose AI at every scale.
