Senior Machine Learning-MLOps Developer

Summary

Join Unity Ads as a Senior Machine Learning Developer and contribute to the Data Services and Moderation team. You will be responsible for transforming ML workflows and ETL pipelines into scalable, production-ready systems that support ad moderation efforts. This role involves collaborating with data scientists, software developers, and product managers to design, develop, and deploy deep learning and LLM/RAG solutions, ensuring their integration into the Unity Ads infrastructure. If you are passionate about driving meaningful monetization for app developers and seeking a challenging and exciting role, this opportunity is for you.

Requirements

• Proficient and strong knowledge in Python, Terraform, cloud platforms (GCP/AWS), Docker, K8, Kafka, Github Actions, serverless, and SQL
• Familiar with Numpy, Tensorflow, Pytorch, vector DB, neural networks, computer vision, LLM, RAG and general ML concepts
• Rich experience in building large-scale streaming ETL pipelines, API endpoints, and serverless apps
• Proficiency in data manipulation and analysis using tools like SQL, BigQuery, Pandas, and/or Spark, along with experience in data visualization and dashboarding
• Strong interpersonal and communication skills, with successful experience collaborating with distributed teams to deliver complex solutions

Responsibilities

• Design, develop, and maintain cloud machine learning pipelines in production for real-time processing and batch processing
• Influence and craft the direction of a highly scalable machine learning pipeline
• Collaborate with data scientists to explore, design, and build pre/post-processing ETL pipelines
• Translate product requirements into a technical design and implement solutions using all resources available to you
• Build, monitor, and maintain CI/CD workflows for internal software/tools

Preferred Qualifications

• Experience with distributed computing libraries such as Ray or Dask
• Experience in A/B testing and statistical analysis
• Experience with ML monitoring solutions (Prometheus, Grafana, etc) in a public cloud environment (GCP, AWS, Azure)