6 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
Liquid AI builds general-purpose AI systems that run efficiently from data center accelerators to on-device hardware, emphasizing low latency, memory efficiency, privacy, and reliability. The company partners with enterprises in consumer electronics, automotive, life sciences, and financial services to deploy and benchmark models for real-world workloads. Founded in 2023 out of MIT CSAIL and headquartered in Cambridge, Massachusetts, it is privately held.