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Job Summary


Job Type
Permanent

Seniority

Years of Experience
Information not provided

Tech Stacks
ETL
Kubeflow
Luigi
Spark
Flink
Airflow
Kubernetes
kafka
C++
Scala
Python
Java

Job Description


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Machine Learning Infrastructure Engineer/ ML Data Engineer

At VIDA we’re building the future of digital identity. As a Machine Learning Infrastructure Engineer you’ll have a key hand in building out the data infrastructure to analyze, train, and serve machine learning models in production to verify and validate our user’s identity. As an early member of the team, you’ll have the ability to shape and build the next-generation data platform that will enable countless users to participate in the online digital ecosystem.

Responsibilities:
  • Design and build fast, flexible, and reliable services for ML model training and serving
  • Design and build a data platform that enables data scientists and analysts to query and slice data for machine learning and surfacing product metrics
  • Build and own scalable machine learning microservices that will power our real-time identity verification stack
  • Collaborate with data scientist, product, engineering, and infra ops teams to deploy easy to use infrastructure for production ML models
  • Work with product and operation teams to ensure that they have the relevant data and metrics to enable better decisions

Requirements:
  • A strong engineering background and experience with data infrastructure and/or distributed systems
  • Experience with or strong interest in developing and deploying ML models in production
  • Expert in Python, C++, Scala, or Java
  • Experience in writing and debugging ETL jobs using a distributed data framework (such as Spark, Kafka, or Flink)
  • Experience with pipeline/task orchestration tools (such as Luigi, Airflow, Kubeflow)
  • Experience with Kubernetes is a plus
  • Take pride in taking ownership and driving projects to have a business impact
  • Thrive in a fast-moving collaborative environment

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