Member of Technical Staff Applied ML RecSys

Liquid AI·Remote(Cambridge)
Other

WFA Digital Insight

As demand for applied machine learning specialists grows, with a notable 25% increase in 2025, Liquid AI is at the forefront of innovation. With a unique approach to general-purpose AI systems, this role offers a rare chance to work on frontier sequential recommendation architectures. To succeed, candidates will need strong skills in data quality, user behavior modeling, and large-scale ranking systems. With the remote job market booming, this is an opportunity to join a cutting-edge company and make a real impact. Before applying, consider your experience with sequential recommendation architectures and ability to optimize for measurable customer outcomes.

Job Description

About the Role

The Member of Technical Staff Applied ML RecSys role at Liquid AI is a unique opportunity to apply machine learning to real-world problems at scale. As a key member of the team, you will own the end-to-end recommendation systems and sequential modeling, working closely with customers to deliver personalized and efficient solutions. This role is perfect for those who are passionate about data quality, user behavior modeling, and large-scale ranking systems.

The day-to-day responsibilities will involve designing and executing data pipelines for user interaction data, feature engineering, and training data curation at scale. You will also fine-tune and adapt large-scale sequential recommendation models for customer-specific use cases, designing task-specific evaluations for recommendation model performance. Your work will have a direct impact on customer outcomes, making this a highly rewarding role for those who enjoy seeing the tangible results of their work.

Liquid AI is a company that values innovation and collaboration, with a strong focus on delivering real-world solutions to complex problems. The team is passionate about AI and its applications, and this role is a great opportunity to join a like-minded group of professionals who are pushing the boundaries of what is possible.

What You Will Do

  • Own customer recommendation system engagements end-to-end, from requirements through delivery and evaluation
  • 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 and interpret results
  • Build reusable applied tooling and workflows that accelerate future customer engagements
  • Act as the technical owner for enterprise customer engagements involving recommendation and ranking workloads
  • Communicate clearly with customers, translating between customer business metrics and internal technical decisions
  • Push back when needed to ensure the best possible outcomes for customers
  • Collaborate with the team to build and adapt large-scale recommendation models for enterprise customers
  • Optimize for measurable customer outcomes, such as engagement, conversion, and revenue lift

What We Are Looking For

  • 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
  • Ability to own customer recommendation system engagements end-to-end
  • Strong communication and collaboration skills
  • Ability to optimize for measurable customer outcomes
  • Experience with transformer-based recommendation architectures
  • Familiarity with serving recommendation models under latency and throughput constraints

Nice to Have

  • Experience delivering recommendation systems to external customers with measurable business outcomes
  • Familiarity with HSTU-style architectures or similar
  • Experience with real-world applications of AI and machine learning
  • Strong understanding of customer business metrics and how to align them with technical decisions

Benefits and Perks

  • Real ML work: building and adapting large-scale recommendation models for enterprise customers
  • Opportunity to work with frontier architectures like HSTU under real production constraints
  • Collaborative and innovative work environment
  • Professional development opportunities
  • Competitive compensation package
  • Flexible remote work arrangements
  • Access to cutting-edge technologies and tools
  • Opportunity to make a real impact on customer outcomes

How to Stand Out

  • When applying, make sure to highlight your experience with sequential recommendation architectures and large-scale ranking systems.
  • Be prepared to discuss your approach to data quality and evaluation design in recommendation contexts.
  • Familiarize yourself with PyTorch and Python, as well as transformer-based recommendation architectures.
  • Showcase your ability to communicate complex technical concepts to non-technical stakeholders.
  • Be prepared to discuss your experience with serving recommendation models under latency and throughput constraints.
  • Research the company and its products to understand how your skills and experience align with their goals.

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