Member of Technical Staff - Applied ML, RecSys
Liquid AI is an efficiency-first foundation-model company building device-native Liquid Foundation Models (LFMs) and tools to customize and deploy them.
Funding history
About Liquid AI
An MIT CSAIL spinout, Liquid AI develops general-purpose AI models focused on efficient deployment across CPUs, GPUs, NPUs, edge devices, and cloud or on-premises environments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own enterprise recommendation system engagements from requirements through delivery and evaluation. You will translate customer needs into model specifications, build data pipelines and training datasets, fine-tune sequential recommendation models, evaluate ranking performance, and create reusable delivery tooling.
Requirements
- Experience building or fine-tuning recommendation models at scale
- Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems
- Knowledge of recommendation data quality and evaluation design, including offline metrics and A/B testing
- Experience with large-scale interaction-data pipelines and feature engineering
- Proficiency in Python and PyTorch
- Autonomous coding and debugging ability
Responsibilities
- Own enterprise recommendation system engagements from requirements through delivery and evaluation
- Translate customer requirements into recommendation model specifications
- Design and execute data pipelines for interaction data, feature engineering, and training-data curation
- Fine-tune and adapt sequential recommendation models
- Design and interpret evaluations for ranking quality, latency, and throughput
- Build reusable applied tooling and workflows
Benefits
- Equity
- Medical, dental, and vision premiums fully paid for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO
- Company-wide Refill Days