Principal Machine Learning Architect

Our Team Values

Take pride in our contributions

Support one another

Optimize what matters

Principled execution

What You’ll Do
Contribute to building a platform to streamline all phases of data-centric innovation, including data organization, data access, data exploration, model prototyping, training, productionisation, testing, and ongoing monitoring of machine learning pipelines

Design infrastructure, perform tool selection, implement utilities and automated workflows to facilitate the operations of a 40+ person, global data science organization

Define cohesive processes and standards in conjunction with tools

Document, communicate, and drive adoption of the ML platform, striving for operational excellence

Collaborate with cross-functional teams to define project execution plans and support the productionisation, deployment, and integration of models and pipelines

Hop into complex data science projects to provide expert guidance, design, and support

Optimize machine learning and data transformation code for scale and robustness

What You'll Bring
8+ years experience working with machine learning models and deploying them into production

Degree in Computer Science, Electrical Engineering, Data Science, or other related computational fields

Experience with NLP, computer vision, or deep learning frameworks such as Tensorflow, PyTorch, or HuggingFace, etc

Expert at Apache Spark, Databricks, Hadoop, or other big data systems

Experience with machine learning frameworks such as Scikit-learn, SparkML, MLFlow, KubeFlow, etc

Experience with batch orchestration frameworks such as Airflow, or streaming services and systems

Experience working with Cloud Platform Technologies in AWS, Google Cloud, or Azure

Have a data-centric mindset and willingness to dive into data with methodical grace

Strong communication skills, both oral and written, and ability to coordinate complex project execution with cross-functional teams

Experience with Scala, Kafka, Apache Beam, Flink, Redis, Docker, or Kubernetes a plus

Experience with recommender systems, entity extraction, or entity resolution a plus