Member of Technical Staff - Applied ML, RecSys
Remote • Boston • FullTime
Posted 6mo ago
Job Location
Boston
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Engineer
About the job
Liquid AI, a rapidly scaling company spun out of MIT CSAIL, is seeking an Applied ML professional to join their team. This role offers a unique opportunity to apply advanced sequential recommendation architectures to real-world enterprise problems at scale. You will be responsible for the end-to-end applied ML work for recommendation system workloads, adapting large-scale models for customers requiring efficient personalization and ranking capabilities under production constraints. This position provides full ownership over model adaptation, evaluation, and deployment, differing from more siloed recommendation roles. You will also contribute to building reusable tooling and workflows to accelerate future projects, making this an ideal role for those passionate about data quality, user behavior modeling, and implementing effective enterprise recommendation systems.
Responsibilities
- 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 for customer-specific use cases.
- Design task-specific evaluations for recommendation model performance, including ranking quality, latency, and 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.
- Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems.
- Strong intuition for data quality and evaluation design in recommendation contexts, including offline metrics, A/B testing, and 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.
- Experience with transformer-based recommendation architectures (e.g., HSTU, SASRec, BERT4Rec).
- Experience delivering recommendation systems to external customers with measurable business outcomes.
- Familiarity with serving recommendation models under latency and throughput constraints.
Benefits
- Competitive base salary
- Equity in a unicorn-stage company
- 100% coverage of medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO
- Company-wide Refill Days