ML Engineer

Role Overview: As part of the Machine Learning (ML) Engineering team, you’ll work with ML Engineers and Data Scientists to support ML applications using high-quality batch and streaming datasets. You’ll engage across the entire ML lifecycle—from exploration to production—while enhancing core infrastructure components to streamline workflows.

Key Responsibilities:

• Collaborate with ML practitioners to understand data requirements.
• Develop and maintain production-grade batch and streaming pipelines.
• Automate ETL jobs using tools like Airflow and Jenkins.
• Improve and extend existing data infrastructure services.
• Conduct data exploration, analysis, and strategy consultation.
• Work in an Agile environment focused on collaboration.
• Mentor colleagues on building large-scale solutions.

Qualifications:

• 5+ years of data engineering experience.
• Experience deploying services in AWS and building big-data solutions using Databricks, EMR, S3, and Spark.
• Proficient in building streaming pipelines with Kafka, Spark, Flink, or Samza.
• Experience with cloud-hosted databases like Redshift and Snowflake.
• Skilled in designing microservices for distributed systems (gRPC or REST).
• Familiar with Apache Airflow or similar graph-based workflows.

Preferred Qualifications:

• Familiarity with ML pipelines and AWS technologies.
• Experience with ML infrastructure and streaming applications.
• Experience shipping entertainment and media applications.