MLOps Engineer | Python | Machine Learning | GCP | Kubernetes | CI/CD | Remote, UK
About Us
We are an early-stage biotechnology company using live cell imaging and artificial intelligence to predict stem cell behavior. Our goal is to improve the manufacturing of human cells for research and therapeutic use by enabling a deeper understanding of cell differentiation. Founded in 2022 as a spin-out from a leading UK research institution, we are backed by prominent venture capital firms with experience investing in AI, life sciences, and deep tech startups.
What We’re Building
At the core of our technology is a proprietary AI-powered SaaS platform that enables scientists to visualize, track, and predict cell differentiation in real-time. Designed to support high-throughput experiments, the platform integrates microscopy, computer vision, cloud infrastructure, and machine learning to accelerate advancements in cell biology and biomanufacturing.
We are currently seeking a
DevOps / MLOps Engineer
to help scale our cloud infrastructure and machine learning workflows as part of our growing technical team.
Key Responsibilities
Develop and maintain infrastructure for our SaaS platform that delivers AI-driven computer vision tools to researchers and scientists.
Collaborate with a multidisciplinary team of machine learning engineers, data scientists, software developers, and biologists.
Build and support GPU-accelerated environments for training and real-time inference of deep learning models.
Deploy and manage ML pipelines using tools like Docker, Kubernetes, and frameworks such as Kubeflow or Ray.
Create and document APIs that enable internal and external users to access data and model outputs.
Implement secure authentication and authorization systems for platform users.
Maintain and improve our cloud platform’s reliability, security, and compliance (e.g., GDPR, HIPAA readiness).
Automate testing, training, and deployment of models through robust CI/CD pipelines.
Monitor and troubleshoot performance issues across data and inference workflows in production.
What We’re Looking For
5+ years of experience in DevOps, MLOps, SRE, or Data Engineering roles.
Strong proficiency with public cloud platforms (e.g., GCP, AWS, or Azure), with preference for GCP.
Expertise in Terraform and infrastructure-as-code practices.
Solid experience deploying workloads with Kubernetes, including cluster and node management.
Familiarity with ML workload orchestration using Docker, Kubeflow, Ray, or similar tools.
Skilled in Python and comfortable working with SQL and data processing tools.
Understanding of the machine learning lifecycle from data ingestion to inference.
Experience handling large-scale datasets and optimizing data pipelines.
Strong communication skills and the ability to clearly document complex systems.
A self-driven mindset and interest in staying current with trends in cloud, data, and ML tools.
Experience leading infrastructure efforts in greenfield or early-stage environments.
Nice to Have
Experience working on SaaS platforms in biotech, healthcare, or life sciences.
Experience with real-time ML inference and production monitoring.
Familiarity with computer vision models and workflows.
Understanding of data privacy regulations and scientific data formats (e.g., TIFF, OME-TIFF).
Background working in early-stage startups (seed or Series A).
What We Offer
Competitive salary and benefits.
Growth opportunities and professional development.
A collaborative, forward-thinking environment at the intersection of AI and biotechnology.
A meaningful mission with the potential to impact healthcare through innovation in science and technology.
Why Join Us
You'll be part of a rapidly growing, interdisciplinary team working on complex challenges in AI and biology. Your contributions will shape the technical foundation of our product and accelerate both scientific discovery and therapeutic development. This is an opportunity to make a tangible impact during a pivotal stage of growth.
MLOps Engineer | Python | Machine Learning | GCP | Kubernetes | CI/CD | Remote, UK
