Seldon is looking for talented Software Engineers with Machine Learning expertise to join our growing Engineering team. This role covers various positions in the software engineering team including backend product, open source MLOps and client facing machine learning engineers and can fit applicants from a range of seniority levels.
 
We are focused on making it easy for machine learning models to be deployed and managed at scale in production. We provide Cloud Native products that run on top of Kubernetes and are open-core with several successful open source projects including Seldon Core, Alibi:Explain and Alibi:Detect. We also contribute to open source projects under the Kubeflow umbrella including KFServing.

We have created a culture that we’re proud of driven by our passionate, talented team and a truly collaborative ethos. Our workplace is engineering-led and design-driven, offering an agile and fast-paced environment that is evolving as we scale, enabling unique opportunities to grow and develop your career as part of the team. 
 
About the role
  • 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.
Essential skills
  • A degree or higher level academic background in a scientific or engineering subject.
  • Familiarity with linux based development.
  • At least 2 years of experience in industry or academia showing completed projects.
  • Interest in MLOps.
Core skills (existing experience or a demonstrable desire to learn)
  • Experience with GoLang and/or Python.
  • Experience with Kubernetes and the ecosystem of Cloud Native tools.
  • Experience using machine learning tools in production.
Bonus skills
  • 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.
Some of the technologies we use in our day-to-day:

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
  • We run the largest Tensorflow meetup in London 
 

Benefits

  • Exciting phase of a start-up with an ambitious team and unlimited potential for professional growth.
  • Share options to align you with the long-term success of the company
  • 28 days paid annual leave 
  • Access to discounted lunches, gyms, shopping and cinema tickets
  • Healthcare cash plan and Employee Assistance Programme
  • Pension scheme 
  • Cycle to work scheme 
Location:
  • London or Cambridge UK offices with a flexible approach to hybrid working  
  • We can provide Visa sponsorship.
Logistics
  • 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.

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