DevOps Engineer

Baseimmune
united kingdom, united kingdom
1 week ago
Create job alert

Role: DevOps Engineer


Keywords: devops, software development, python, vaccines


Team:Computational Engineering Team

Start date:Immediate

Period:Full time, permanent

Location:Baseimmune, APEX, 6 St. Pancras way, London (Hybrid or Fully Remote – UK only)

Closing Date:29 April 11am (GMT)


About us

Baseimmune is a discovery stage biotechnology start-up focused on revolutionising vaccine development through advanced computational antigen design. Founded by the team that pioneered computational vaccines at the Jenner Institute University of Oxford, our mission is to harness the fields of data science, machine learning and computational biology within a multi-disciplinary team to redefine cross-protective and mutation-proof vaccines.


Position Overview

We are seeking a highly motivated and skilledDevOps Engineerto join an interdisciplinary team located at APEX in King’s Cross, London.This is a full-time rolewith the opportunity for fully remote or hybrid working style. The engineering team expects to have in-person workshops in London at least 4 times per year.


For this role, we are looking for a skilled DevOps Engineer to build and optimize our on-site, co-located and hybrid cloud infrastructure, ensuring seamless integration between our AI-driven research, high-performance computing, and laboratory workflows. You will work closely with our software engineers, bioinformaticians and data scientists to enable scalable, secure, and efficient development and scientific processes. You should have experience in scaling services and infrastructure as either a solo effort or with a small team.


As the new member of a growing Engineering Team you will have the opportunity to contribute and develop core software and tooling that enables the creation of novel computationally designed vaccines.


Key Responsibilities

  • Maintain and manage company owned hardware / software systems.
  • Develop automation to support streamline software development (e.g., CI/CD and configuration management).
  • Design, develop, and deploy new computational tools and processes to support various aspects of our biotech research and development projects.
  • Monitor system performance and troubleshoot issues.
  • Support and develop big data workflows.
  • Support our growing Computational Sciences Team as a key member of a fast-moving biotech startup.


Required Qualifications and Skills

  • 2-5 years of experience in DevOps with a preference for Bioinformatics environments or similar big data research disciplines.
  • Proficient with the Linux/Unix environment, tooling and user account administration.
  • Proficient with Git and Git platforms (e.g., GitLab / GitHub) and CI/CD automation.
  • Proven self-starter with keen problem-solving skills and the ability to work independently as well as part of a team.
  • Experience in developing with Python and knowledge of Python development and ecosystem (e.g., pip, Poetry, uv, etc.)


Desired Qualifications and Skills

  • Familiarity with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and related services/tools.


Soft Skills

  • You are open and highly flexible in dealing with rapidly evolving tasks and demands and see them as an opportunity to proactively shape our processes.
  • Team-oriented personality, able to build strong relationships in a diverse environment, work effectively as part of a collaborative team, but also execute tasks independently when needed.
  • Inclusive, collaborative, and thrives in a team environment with a flexible approach to working and the ability to work in a fast-paced environment, across different projects and areas.
  • Effective communication skills with the confidence and judgement to challenge appropriately.
  • Analytical and ambitious personality, able to proactively apply knowledge, structured problem-solving skills, and creative thinking in driving the project forwards and helping others.


What we Offer

  • Competitive starting salary
  • Central London location for lab and office; this role can be either fully remote or a hybrid regimen
  • Private medical health insurance
  • Opportunities for career growth and professional development
  • 26 days holiday leave + bank holidays
  • Enhanced sick pay
  • Family friendly policies, including enhanced maternity, paternity and adoption leave
  • Pension plan


Please note that Baseimmune does not provide visa sponsorship for this position. Applicants must be eligible to work in the UK without requiring employer sponsorship. We encourage all eligible candidates to apply and regret that we are unable to consider candidates who require visa sponsorship currently.


We are an equal opportunity employer and value diversity and inclusion in our workplace.


Privacy statement – readhereabout how we collect and use your data.

Related Jobs

View all jobs

DevOps Engineer

DevOps Engineer

DevOps Engineer

Business Analyst (DV Cleared)

Machine Learning Engineer · · (Basé à London)

Data Science Lead

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.