Senior ML Engineer - Drive Production-Ready Personalization

Bumble Inc.; Austin (TX); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Stock options

Flexible work hours

Job summary

Bumble Inc. in Austin, TX is seeking an experienced Senior ML Engineer to build and deploy machine learning models that enhance recommendations, ranking, and personalization across the Bumble platform.

You will own end-to-end ML projects, develop scalable pipelines with Spark and Airflow, apply PyTorch or TensorFlow in production, and collaborate with Product and Engineering to translate questions into impactful ML solutions.

Qualifications

• Typically requires 5-8 years of experience.
• Strong experience building and deploying ML models in production environments.
• Proficiency in Python and ML frameworks (e.g. PyTorch or TensorFlow).
• Experience with data pipelines and distributed systems (e.g. Spark, Airflow).
• Familiarity with A/B testing and model evaluation techniques.
• Ability to collaborate across functions with ownership and collaboration.
• Growing AI fluency and responsible AI practices.

Responsibilities

• Build and deploy machine learning models improving recommendations, ranking, and personalization.
• Own problems end-to-end from data exploration to production deployment.
• Develop and maintain scalable ML pipelines using Spark and Airflow.
• Apply modern ML frameworks (PyTorch or TensorFlow) in production environments.
• Contribute to experimentation frameworks including A/B testing and offline evaluation.
• Collaborate with Product and Engineering to translate questions into ML solutions.

Skills

Python

ML in production

Cross-functional collaboration

Experimentation

Agile mindset

Responsible AI

Tools

PyTorch

TensorFlow

Spark

Airflow

Job description

Bumble Inc. in Austin, TX is seeking an experienced Senior ML Engineer to build and deploy machine learning models that enhance recommendations, ranking, and personalization across the Bumble platform.

You will own end-to-end ML projects, develop scalable pipelines with Spark and Airflow, apply PyTorch or TensorFlow in production, and collaborate with Product and Engineering to translate questions into impactful ML solutions.