Senior Machine Learning Engineer
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
One last thing — add a quick note at the bottom of your resume (1–3 lines): what drew you to Graile