Member of Technical Staff - Applied ML, RecSys

Liquid AI
Cambridge, Boston
Workplace: HybridFull timeFunction: OtherSkills: ["Ownership","Communication","Problem-solving","Collaboration","Critical thinking"]

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.

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FursaFursa
Liquid AI
Liquid AI
5 months ago

Member of Technical Staff - Applied ML, RecSys

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Source: Company careers pageValidated by: Fursa AI
Last checked: 7 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
Builds AI infrastructure and tooling to enable real-time, distributed machine learning and orchestration across edge and cloud environments, simplifying deployment and management of intelligent applications.
Industry: AI & Machine Learning
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