Lead Applied ML Engineer, Enterprise Recommendation Systems

liquid-ai; Boston (MA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Real ML work

Competitive base salary with equity

Full health coverage

401(k) matching

Unlimited PTO

Job summary

Liquid AI is seeking a senior ML engineer to own end-to-end enterprise recommendation engagements and adapt large-scale models under production constraints. You will translate customer requirements into model specs and build scalable data pipelines for user interaction data and feature engineering.

You will fine-tune seq. recommender models and design evaluations for latency, throughput, and business impact, while developing reusable tooling for future engagements.

Qualifications

• 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.
• Experience with large-scale data pipelines for user interaction data and feature engineering.
• Proficiency in Python and PyTorch with autonomous coding and debugging ability.
• Nice-to-have: transformer-based architectures (e.g., HSTU, SASRec, BERT4Rec).
• Experience delivering recommendation systems to external customers with measurable business outcomes.

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 model performance (ranking quality, latency, throughput) and interpret results.
• Build reusable applied tooling and workflows that accelerate future customer engagements.

Skills

Rec models at scale

Sequential rec architectures

Data quality & evaluation

Data pipelines

Python & PyTorch

Tools

Python

PyTorch

Job description

Liquid AI is seeking a senior ML engineer to own end-to-end enterprise recommendation engagements and adapt large-scale models under production constraints. You will translate customer requirements into model specs and build scalable data pipelines for user interaction data and feature engineering.

You will fine-tune seq. recommender models and design evaluations for latency, throughput, and business impact, while developing reusable tooling for future engagements.