4 months ago

Member of Technical Staff - Applied ML, RecSys

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Applied ML specialist focused on end-to-end enterprise recommendation systems, owning data pipelines, model fine-tuning, and evaluation for large-scale ranking tasks. You’ll adapt foundation models for customers, design scalable workflows, and ensure measurable business impact (engagement, conversion, revenue lift) while delivering practical, production-ready solutions.

Location: Cambridge, Boston

Workplace: Hybrid

Employment Type: Full time

Seniority: Mid level

Key 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 (e.g., HSTU-style architectures) for customer-specific use cases
•Design task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret results
•Build reusable applied tooling and workflows that accelerate future customer engagements

Pay and Benefits

Equity and Bonus:Equity

Perks:Health Insurance401kPaid Leave

Key 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 (offline metrics, A/B testing, 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:Recommender systemsEnterprise softwareML workflows

Skills:OwnershipCommunicationProblem-solvingCollaborationCritical thinking

Languages:English

Tech Stack:PythonPyTorchData pipelinesSequential modelsHSTU

Company Brief

Liquid AI

Industry: AI & Machine Learning

Website

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Liquid AI

4 months ago

Member of Technical Staff - Applied ML, RecSys

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: Cambridge, Boston

Workplace: Hybrid

Employment Type: Full time

Seniority: Mid level

Key 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 (e.g., HSTU-style architectures) for customer-specific use cases
•Design task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret results
•Build reusable applied tooling and workflows that accelerate future customer engagements

Pay and Benefits

Equity and Bonus:Equity

Perks:Health Insurance401kPaid Leave

Key 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 (offline metrics, A/B testing, 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:Recommender systemsEnterprise softwareML workflows

Skills:OwnershipCommunicationProblem-solvingCollaborationCritical thinking

Languages:English

Tech Stack:PythonPyTorchData pipelinesSequential modelsHSTU

Company Brief

Liquid AI

Industry: AI & Machine Learning

Website