TL;DR

Senior Machine Learning Engineer (AI): Building and optimizing machine learning pipelines and production systems for recommendation models with an accent on scalability and productionisation. Focus on diagnosing production ML issues, implementing monitoring strategies, and contributing to ML infrastructure evolution.

Location: This role is based in Austin, and we ask that you’re within a commutable distance to this office, so that you’re able to come onsite regularly to collaborate across engineering teams.

Salary: $145,000 - $175,000 a year

Company

%hirify_global% is the parent company of Bumble, Badoo, Bumble For Friends, and Geneva, enabling people to build healthy and equitable relationships.

What you will do

Design, build and optimise ML pipelines and production systems that train, evaluate and serve recommendation models efficiently and at scale.
Work in a cross-functional team alongside data scientists, machine learning scientists, software engineers and both technical and non-technical stakeholders.
Partner with ML Scientists to translate research models into efficient, maintainable, and well-tested production systems.
Implement monitoring, observability, and retraining strategies to ensure continuous model performance in a dynamic, global environment.
Contribute to the evolution of our ML infrastructure, including CI/CD, model registries, and feature stores.
Diagnose and resolve production ML issues, such as data inconsistencies and model drift, to identify and resolve infrastructure bottlenecks.

Requirements

2+ years of relevant industry experience.
An advanced degree in Computer Science, Mathematics or a similar quantitative discipline.
Strong software engineering background. You write clean, scalable, and maintainable code in Python or similar languages.
Proven experience deploying and operating ML systems in production environments.
Deep understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning.
Experience with modern ML frameworks (e.g. PyTorch, TensorFlow) and orchestration tools (e.g. Airflow, Kubeflow, SageMaker, Ray).