Staff Machine Learning Engineer

Goatgroup; United States; On-site; USD 159,100 - 233,800; Full time

Benefits offered by this job

401K

Paid time off

Dental

Medical

Vision

Disability insurance

Life insurance options

Job summary

GOAT Group is seeking a Staff Machine Learning Engineer to architect and own end-to-end ML systems powering search, ranking and recommendations across our marketplace. You’ll manage production pipelines from training to deployment with a focus on reliability and scalable infrastructure.

Collaborating with data science, data engineering, product and backend teams, you will set technical standards, evaluate infra decisions, and ensure models remain healthy through monitoring and retraining in a

Qualifications

• 7+ years of engineering experience with production ML systems.
• End-to-end ownership: training pipelines through deployed inference.
• Advanced knowledge of ML/AI and their ecommerce applications.
• Strong proficiency in Python; SQL; DBT; Airflow or similar.
• Experience with ranking, retrieval, or recommendation systems.
• Expertise with ML lifecycle tooling (experiment tracking, model versioning, drift detection).

Responsibilities

• Own the full lifecycle of predictive models in production — architecture, training pipelines, inference, deployment and model health.
• Build and operate systems that route model outputs into live product surfaces (search, recommendations, feeds).
• Establish and maintain model monitoring, drift detection, and retraining cadences.
• Partner with Data Science, Data Engineering, Product, and Backend teams to move work from validated approach to production system.
• Decide on whether to leverage ML infrastructure from GOAT Group or build in-house solutions.
• Contribute to ML infrastructure decisions — serving architecture, feature computation, and orchestration.
• Set technical standards for ML systems across the pod.

Skills

Python

SQL

DBT

Airflow

Ranking systems

End-to-end ownership

ML lifecycle tooling

Tools

Experiment tracking

Model versioning

Pipeline orchestration

Drift detection

Cloud data warehouse

Search/retrieval systems

Job description

ROLE OVERVIEW

Grailed is looking for a Staff Machine Learning Engineer to help us build the models and systems that connect buyers to the inventory they're looking for — and surface things they didn't know they wanted. Our data sits at the center of a complex peer-to-peer marketplace, and the ML layer is what turns a decade of behavioral signals into better search, smarter recommendations, and a marketplace that gets sharper over time.

This is a hands‑on technical role for an engineer who takes end‑to‑end ownership seriously — from architecture through production operation — and who is energized by working on a small, focused team where the infrastructure is still being built and the decisions made now have lasting consequences.

The strongest candidates will bring production instincts alongside technical depth: the kind of engineer who isn't done when the model trains, and who treats monitoring, retraining, and reliability as part of the job, not a follow‑on task.

What You'll Do

• Own the full lifecycle of predictive models in production — architecture, training pipelines, inference infrastructure, deployment, and ongoing model health
• Build and operate the systems that route model outputs into live product surfaces: search ranking, recommendations, feed ordering, and related user‑facing experiences
• Establish and maintain model monitoring, alerting, drift detection, and retraining cadences — the feedback loops that keep deployed models accurate over time
• Partner closely with Data Science, Data Engineering, Product Management, and backend engineering to move work from validated approach to production system
• Own the decision‑making process on whether to leverage ML infrastructure & expertise from our parent company, GOAT Group, and when to advocate for building in‑house solutions.
• Contribute to ML infrastructure decisions — serving architecture, feature computation, pipeline orchestration — with an eye toward what scales as the team and model count grows
• Set technical standards and raise the bar for how ML systems are built, evaluated, and operated across the pod

Technical Requirements

• 7+ years of engineering experience, with substantial depth in production machine learning systems.
• Demonstrated end‑to‑end ownership: training pipelines through deployed inference, not just modeling.
• Advanced knowledge of ML, AI and statistical models, as well their application in e‑commerce settings.
• Strong proficiency in Python; SQL; DBT; airflow or similar.
• Solid software engineering fundamentals.
• Experience with ranking, retrieval, or recommendation systems.
• Demonstrated expertise with ML lifecycle tooling — experiment tracking, model versioning, pipeline orchestration, drift detection — and comfort working with modern data infrastructure (cloud warehouse, search/retrieval systems).

What We're Looking For

• Takes ownership of developing repeatable end‑to‑end processes, not just outcomes
• Evaluates technical approaches against production constraints — latency, reliability, retraining cost — not just offline metrics
• Brings judgment to architecture decisions: knows when to reach for a complex approach and when a simpler one is the right call
• Treats model health as a permanent responsibility, not a launch milestone
• Communicates clearly with non‑technical partners — can translate model behavior, tradeoffs, and timelines into terms that product and business stakeholders can act on
• A willing collaborator who keeps people informed and works through ambiguity without going quiet
• Genuine curiosity about the domain — fashion, resale, taste — and the specific ML problems it creates

Nice To Have

• Experience with semantic enrichment, NLP, or multi‑modal ML in a production context
• Genuine curiosity about the domain — fashion, resale, style — and the specific ML problems it creates

Compensation and Benefits

GOAT Group uses geographic pay tiers based on the employee’s home state to align compensation with market differences across the U.S.

Tier 1 (Includes states such as California, New York (including New York City), Washington, Illinois and other higher‑cost markets): $187,100 - $233,800 USD

Tier 2 (Includes mid‑cost markets across the U.S.): $168,500 - $210,600 USD

Tier 3 (All other U.S. locations): $159,100 - $198,800 USD

The hiring range for this position is below, plus benefits (401K, paid time off, dental, medical, vision, disability, life insurance options).

GOAT Group will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance, if applicable.