Golang Job: Machine Learning Engineer

• Help realise the product vision: Production-ready machine learning models within moments, not months. Our products make enterprise-grade MLOps easy.
• Help design, build and extend Seldon's core product range of MLOps (Machine learning operations) tools and products.
• Help enterprises deploy their machine learning models at scale across a wide range of use-cases and sectors.
• Extend the state of the art in the developing area of MLOps including:
  • Managing the production lifecycle of ML models from initial deployment, to testing and updating of the next iteration.
  • Monitoring ML models in production.
  • Explaining and ensuring correct governance of ML models in production.

• A degree or higher in a scientific or engineering subject or equivalent relevant experience
• Familiarity with linux based development.
• At least 2 years of experience in industry or academia showing completed projects.
• Interest in MLOps.

• Experience with GoLang and/or Python.
• Experience with Kubernetes and the ecosystem of Cloud Native tools.
• Experience using machine learning tools in production.

• Contributions to open source projects
• A broad understanding of data science and machine learning.
• Understanding of explainable AI or machine learning monitoring in production.
• Familiarity with Kubeflow, MLFlow or Sagemaker.
• Familiarity with python tools for data science.

• Go is our primary language for all-things backend infrastructure including our Kubernetes Operator, and our new GoLang Microservice Orchestrator)
• Python is our primary language for machine learning, and powers our most popular Seldon Core Microservices wrapper, as well as our Explainability Toolbox Alibi
• We leverage the Elastic Stack to provide full data provenance on inputs and outputs for thousands of models in production clusters
• Metrics from our models collected using Prometheus, with custom Grafana integrations for visualisation and monitoring
• Our primary service mesh backend leverages the Envoy Proxy, fully integrated with Istio, but also with an option for Ambassador
• We leverage gRPC protobufs to standardise our schemas and reach unprecedented processing speeds through complex inference graphs
• We use React.js for our all our enterprise user products and interfaces
• Kubernetes and Docker to schedule and run all of our core cloud native technology stack

Some of our high profile technical projects

• We are core authors and maintainers of Seldon Core, the most popular Open Source model serving solution in the Cloud Native (Kubernetes) ecosystem
• We built and maintain the black box model explainability tool Alibi
• We are co-founders of the KFServing project, and collaborate with Microsoft, Google, IBM, etc on extending the project
• We are core contributors of the Kubeflow project and meet on several workstreams with Google, Microsoft, RedHat, etc on a weekly basis
• We are part of the SIG-MLOps Kubernetes open source working group, where we contribute through examples and prototypes around ML serving

• A supportive and collaborative team environment
• A commitment to learning and career development and £1000 per year L&D budget
• Flexible approach to hybrid-working (2/3)
• Share options to align you with the long-term success of the company
• 28 days annual leave (plus flexible bank holidays on top)
• Perkbox - perks, medical and wellbeing benefits
• Healthcare cash plan and Employee Assistance Programme
• Pension scheme
• Cycle to work scheme

• Our interview process is normally 4 filtered stages:
  • a 30min phone interview.
  • a coding task.
  • 2-3 hours of post-task interview.
  • final interviews.
• Our recruitment process has an average length of 3 weeks.
• As part of the process we will identify which part of the tech team fits your skills and interests more closely: product, delivery, MLOps. However, as we are a small team, all our employees are very cross functional and roles develop based on skills, interests and ongoing projects.