Seldon was founded in 2014 with a simple yet ambitious mission: accelerate the adoption of machine learning to solve the world's most challenging problems. Seldon accelerates the process of deploying, monitoring, explaining, and managing machine learning models, reducing risk and increasing ROI for data science projects. We have already helped to bring more than 3+ million unique ML models to production for hundreds of enterprises including Capital One, Ford, and Exscientia with productivity gains of up to 92%.
Machine learning will soon be at the core of every connected business, so we're seeking talented individuals to drive our mission forward to deliver industry-leading machine learning deployment and continue to make our mark in the MLOps space.
We've created a culture that we're proud of that's driven by our open, collaborative ethos and supportive environment. We're a passionate team of innovators and the builders of serious next-generation, data-centric MLOps.
Seldon is looking for talented software engineers to join our team at our Cambridge office.
What You Will Be Doing
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.
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 looking to join Seldon's growing engineering team.
We have created a culture that we're proud of driven by our passionate, talented team and our open, collaborative ethos. We operate on the cutting edge of technology, in an agile environment that is evolving as we scale, enabling unique opportunities to grow and develop your career as part of the team and help shape the future with MLOps.
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.
- Experience 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 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
- And much more
Some of the technologies we use in our day-to-day:
- 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
Location:
- Cambridge UK.
- We have a hybrid home/office work setup. It would be expected you are able to travel to our London head office (Old Street area) around once per week to work with the rest of the Seldon team.
- We can provide Visa sponsorship.
Benefits
- An exciting role in a fast-growing company, with the opportunity to play a key part in growing our business
- A supportive and collaborative team environment
- A commitment to learning and career development and £1000 per year L&D budget
- Share options to align you with our long-term success
- 28 days annual leave (plus flexible bank holidays on top)
- Nest Pension scheme
- AXA Private Medical Insurance (including dental and optical)
- AIG Life Assurance and EAP
- Perkbox - perks and wellbeing benefits
- Cycle to work scheme
- Regular socials, lunch & learns and games nights
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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