Machine Learning Infrastructure Engineer

About the Role

Moloco is a machine learning company operating at massive scale (we ingest 10 petabytes of training data per day) and our models are blazingly fast (returning predictions in 10 milliseconds or less); and a profitable unicorn (we're valued at $2B & been profitable for the last 13 quarters). 

We are seeking an exceptional Senior Software Engineer to join us in building state-of-the-art machine learning infrastructure for our mobile advertising platform. You will be part of the engineering team that builds systems to train deep-neural-network machine learning (ML) models, as well as the infrastructure to deploy them for real-time, high-scale ad targeting.

We’re looking for a seasoned engineer to design, build and support the machine learning platforms that will power the future of Moloco’s growth. Your work will span our flagship user- and advertiser-facing products. Relevant technologies we use at Moloco include Tensorflow, Airflow, BigQuery, Dataflow, Kubernetes, Google Cloud Platform, Terraform and the C , Go and Python programming languages.

What you'll do

• Build systems to train and serve dozens of ML models updated hundreds of times per day
• Use state-of-the-art hardware accelerators (e.g. GPUs) to optimize ML training & inference
• Deploy and respond to system alerts to maintain Moloco's real-time large-scale ML-based advertising platform in healthy operation
• Leverage your knowledge of cloud infrastructure to optimize the cost, reliability and maintainability of our platform
• Collaborate with ML modeling engineers and other partners in the company to build the best mobile advertising platform in the world

What you'll need to succeed

• 4 years software development experience on backend/infrastructure systems, in C#, C , Go, Java or Python
• 1 years of cloud experience (deep expertise in using GCP, AWS and/or Azure)
• Strong Computer Science fundamentals and problem-solving skills
• Strong software development skills, from requirements to design to implementation
• Mastery of multiple skills involved in developing large scale systems such as integration testing, deployment, monitoring, production troubleshooting, database schema design, or developing data processing pipelines
• Strong written and verbal communication skills
• Bachelor’s Degree in Computer Science/Engineering
  
  Preferred Qualifications

• Experience with machine learning frameworks (e.g. TensorFlow, PyTorch)
• Experience with machine learning model architectures
• Experience with ML hardware accelerators (e.g. GPUs, TPUs)