Member of Technical Staff - Applied ML, RecSys

Liquid AI

Boston (MA)

On-site

USD 140,000 - 210,000

Full time

14 days+

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Benefits offered by this job

Real ML work

Equity in unicorn-stage company

Health premiums for employees and deps

401(k) matching up to 4%

Unlimited PTO

Refill Days

Job summary

Liquid AI is seeking an experienced applied ML leader to own enterprise recommendation engagements end-to-end, from requirements to delivery and evaluation. You will adapt and deploy large-scale sequential models for customers, building reusable tooling and workflows to accelerate future engagements.

You will reason about user interaction data, model latency, and business impact, translating customer metrics into technical decisions and delivering measurable value in production environments.

Qualifications

Must own enterprise recommendation engagements end-to-end, from requirements through delivery and evaluation.
Strong ability to reason about user interaction data, sequential modeling, feature engineering, and evaluation at scale.
Demonstrated pragmatism, optimizing for measurable customer outcomes (engagement, revenue lift).
Excellent ability to translate customer metrics into technical decisions and communicate with stakeholders.

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 to accelerate future engagements

Skills

Ownership mindset

Scalability thinking

Communication skills

Python

PyTorch

Tools

HSTU architectures

SASRec

Job description

About Liquid AI

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

The Opportunity

This is a rare chance to apply frontier sequential recommendation architectures to real enterprise problems at scale. You will own applied ML work end-to-end for recommendation system workloads, adapting Liquid Foundation Models for customers who need personalization and ranking capabilities that run efficiently under production constraints.

Unlike most recommendation roles that are siloed into a single product surface, this role gives you full ownership over how large-scale recommendation models are adapted, evaluated, and deployed for enterprise customers. Between engagements, you will build reusable applied tooling and workflows that accelerate future delivery.

If you care about data quality at scale, user behavior modeling, and making recommendation systems actually work in enterprise production environments, this is the role.

What We’re Looking For

We need someone who:

Takes ownership: Owns customer recommendation system engagements end-to-end, from requirements through delivery and evaluation.

Thinks at scale: Can reason about user interaction data, sequential modeling, feature engineering, and evaluation across large-scale production systems.

Is pragmatic: Optimizes for measurable customer outcomes (engagement, conversion, revenue lift) over theoretical novelty.

Communicates clearly: Can translate between customer business metrics and internal technical decisions, and push back when needed.

The Work

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

Desired Experience

Must-have:

Hands‑on experience building or fine‑tuning recommendation models at scale (not just off‑the‑shelf collaborative filtering)

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

Nice‑to‑have:

Experience with transformer‑based recommendation architectures (HSTU, SASRec, BERT4Rec, or similar)

Experience delivering recommendation systems to external customers with measurable business outcomes

Familiarity with serving recommendation models under latency and throughput constraints

What Success Looks Like (Year One)

Independently owns and delivers enterprise recommendation system engagements with minimal oversight

Is trusted by customers as the technical owner, demonstrating strong judgment on the tradeoffs between model quality, latency, and business impact

Has built reusable applied workflows or tooling that accelerate future customer engagements

What We Offer

Real ML work: You will build and adapt large-scale recommendation models for enterprise customers, working with frontier architectures like HSTU under real production constraints.