Staff Machine Learning Engineer

What you'll do Evolve a scalable ML platform to train large models on billions of samples Evolve a cutting edge, high performance, high throughput, low latency serving and experimentation platform Solve extremely hard problems from scratch, building new ML pipelines, exploring and possibly adopting new technologies Design and implement ML tooling and frameworks to accelerate the development and experimentation cycle Coach other teammates and become a reference for technical implementation Prioritize the value generated to the business, beyond the performance of the solutions What you'll need 5+ years of professional experience in Applied Machine Learning Experience in Extreme Scaling ML, running distributed training and validation techniques on large models (+100 MM params) fit on PBs of data Experience deploying, serving, and running experimentation on a high throughput – low latency environment Advanced knowledge of Machine Learning toolkits (scikit-learn, MLLib, TensorFlow, PyTorch) Excellent analytical, problem-solving and critical thinking skills Proficiency with MLOps technologies and stacks (Airflow, Kedros, Spark, Databricks, MLFlow, TensorFlow Extended (TFX), KubeFlow, Jenkins, Gitlab CI/CD, Terraform) Great coding skills, proficiency with distributed computing frameworks (Spark, Presto, Hive, Horovod) Great communication skills both written and oral College degree in Computer Science, Statistics, Mathematics, a related field, or equivalent relevant experience Nice to Have 7+ years of professional experience working as a Applied Machine Learning Experience developing systems in the ad tech ecosystem Proficiency with Cloud based ML platforms (AWS Sagemaker, GC AutoML, Azure ML, Databricks) Experience in time series, hierarchical models, previous experience with product analytics Active contribution to ML open source projects Active participant on the ML community