Sr. Machine Learning & Infrastructure Engineer

Appen Limited
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Job Description

Artificial Intelligence is transforming the world in almost every industry. Everyone knows only good training data can produces the best machine learning solutions. However, creating training data with high quality in a scalable way is very challenging and very few company can do it. The whole AI world is starving for great training data. Appen is the market lead in training data generation field for more than 22 years and generates all kinds of training data like content relevance, image & video, text and audio, as well as data capturing.

Appen Tech team is solving the AI data problem by combining the power of humans and technology. This world-class and exciting engineering position awaits a qualified candidate who will join Appen’s fast growing data science & machine learning team.

We are seeking a staff/senior level machine learning infrastructure engineer with hands-on expertise to build and enhance our machine learning infrastructure to improve millions of annotators efficiency at scale. This is a tech lead role with the potential to grow into a senior manager.

In this role, you will be the part of a fast growing team solving very interesting technical problems at the intersection of various exciting domains like Cloud Computing, Distributed Systems, Big Data, Machine Learning, and High Performance Computing. Your work will have an enormous impact on Appen's long-term success.


  • Design solutions for machine learning infrastructure that will support the current and future needs of our business. 
  • Build scalable and reliable infrastructure that supports feature engineering, model training, deployment, inferencing, performance monitoring in the cloud and on prem.
  • Build pipeline that supports running multiple machine learning models in parallel in production.
  • Collaborate with data scientists to improve existing machine learning model training and evaluation pipelines.
  • Build continuous integration, testing, and deployment pipelines in cloud computing environments for machine learning services.
  • Build data logging, tracking, analyzing, monitoring and reporting pipelines in cloud computing environments.
  • Work closely with related teams across Appen to ensure that our machine learning services work seamlessly in Appen's platforms and tools.

Required Knowledge, Skills and Abilities:

  • BS or above in Computer Science, Electrical Engineering, or a related field
  • 5+ years of industry experience building ML infrastructure at scale
  • Proficient in Python or C++. Experience writing and maintaining high-quality production code
  • Deep knowledge of Linux operating system and proficient in Bash
  • Experience in using AWS ECS (or similar orchestration platforms such as Kubernetes), Kafka (or similar streaming platforms), low latency data management systems such as Redis, RocksDB, DynamoDB
  • Experience in using AWS and/or other major cloud platforms to build data-intensive infrastructure, specially for machine learning applications
  • Working knowledge of CI/CD automation tools such as Docker, Jenkins, Terraform, Helm etc.
  • Working knowledge of publicly available ML platforms such as Sagemaker, Kubeflow etc.
  • Knowledge of machine learning concepts and fundamentals
  • Great communication skills, both written and oral; comfortable presenting findings and recommendations to non-technical audiences
  • Ability to proactively learn new concepts and apply them at work

Company Info.

Appen Limited

Appen Limited is a publicly traded data company listed on the Australian Securities Exchange under the code APX. Appen provides or improves data used for the development of machine learning and artificial intelligence products.

  • Industry
    Information Technology
  • No. of Employees
  • Location
    Chatswood New South Wales, Australia
  • Website
  • Jobs Posted

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Appen Limited is currently hiring Senior Machine Learning Infrastructure Engineer Jobs in United States with average base salary of $120,000 - $190,000 / Year.

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