Liquid AI

Liquid AI

Member of Technical Staff - Applied ML, RecSys

Boston · Staff+

Sponsorship not specifiedDetected 200 days ago
PythonMachine LearningPyTorchData EngineeringNLPA/B Testing

Stay score

odds of building a lasting career here

36Risky
Cap-exempt (no lottery)0
Sponsors this role74
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.

Lottery odds assume a STEM candidate.

Personalize to your clock →

Employer immigration record

from this employer's Department of Labor filings

Files H-1B transfers

2 transfer filings in the last year, covering 2 workers. Median labor-condition decision: 22 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

Community outcomes

No reports yet — be the first to help the next applicant.

About the role

  • This is a rare chance to apply frontier sequential recommendation architectures to real enterprise problems at scale.
  • 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.
  • 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.

Responsibilities

  • 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.
  • Between engagements, you will build reusable applied tooling and workflows that accelerate future delivery.
  • 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.

Requirements

  • Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems
  • 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)
  • Familiarity with serving recommendation models under latency and throughput constraints
  • Has built reusable applied workflows or tooling that accelerate future customer engagements

Compensation

  • Competitive base salary with equity in a unicorn-stage company
  • We pay 100% of medical, dental, and vision premiums for employees and dependents
  • 401(k) matching up to 4% of base pay

Benefits

  • Compensation: Competitive base salary with equity in a unicorn-stage company
  • Health: We pay 100% of medical, dental, and vision premiums for employees and dependents
  • Time Off: Unlimited PTO plus company-wide Refill Days throughout the year

Company info

  • We are scaling rapidly and need exceptional people to help us get there.
  • We partner with enterprises across consumer electronics, automotive, life sciences, and financial services.
  • 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.

This listing is sourced directly from Liquid AI's careers page and normalized into a canonical job model.