Varo Bank
We're on a mission to bring financial inclusion and opportunity to all.
Mobile Banking • User Interface • Financial Services • Risk Management • Design Research
501 - 1000
Sr. Machine Learning Engineer
August 28
🏡 Remote – Anywhere in California
Airflow
AWS
Cloud
Docker
DynamoDB
Grafana
GRPC
Java
Kafka
Keras
Kotlin
Kubernetes
Pandas
Postgres
Python
Scala
Scikit-Learn
Spark
Spring
Tensorflow
Terraform
Varo Bank
We're on a mission to bring financial inclusion and opportunity to all.
Mobile Banking • User Interface • Financial Services • Risk Management • Design Research
501 - 1000
Description
• Build feature pipelines/embeddings for batch and real-time data • Add functionality for the data science team to improve model training, evaluation, and batch inference at scale • Build and deploy production ML models (model training, batch and real time inference) • Improve model monitoring and alerting • Work on services that are implemented in Python and Java/Kotlin • Accelerate ML development as the team scales up alongside Varo’s increasing adoption of ML solutions • Design, implement, and own APIs and integrations between our ML model inference services and the company’s broader engineering systems • Design, develop, and advocate for ML platform observability, scalability, security, and performance • Work in a cross-functional capacity with data science, product, and business stakeholders • Solve problems in many different areas, including risk, fraud, lending, engagement, operations, and customer acquisition
Requirements
• Must be proficient in a JVM language (Java, Kotlin, Scala) — technical interview will be in one of these languages • Bachelor's degree (or foreign equivalent) in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field & 3 years of experience involving machine learning systems and model building. • Strong programming and software engineering skills • Experience with some of the following technologies: Java/Kotlin, Python, gRPC, Terraform, Spring, AWS (Sagemaker, Glue), Kubernetes, Airflow, Spark, databases (DynamoDB, Postgres, Athena), Kafka, Docker, ML tools/frameworks (Keras, Tensorflow, SparkML, pandas, scikit-learn, etc.), Grafana, Kibana • Collaborating with cross-functional teams (e.g. making complex ML concepts easy to understand, getting buy-in from stakeholders, getting and giving feedback to teammates, etc.) • Master’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field is preferred
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