Staff Machine Learning Engineer
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Information
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
Aplica aquí