Senior Backend Engineer (MLOps)

Optimove; Dundee; On-site; GBP 50,000 - 75,000; Full time

Job summary

A leading marketing tech company is seeking a Senior Software Engineer to join their MLOps team in Dundee. This role involves developing robust pipelines for machine learning models and requires strong skills in backend engineering and ML system architecture. You'll work collaboratively within a dynamic R&D environment, focusing on optimizing performance, reliability, and deployment of ML initiatives.

Qualifications

• 4+ years of experience in backend engineering or high-performance software development.
• Strong proficiency in low-level programming languages required.
• Familiarity with ML system architecture and cloud platforms preferred.

Responsibilities

• Architect and develop pipelines for ML model training and deployment.
• Implement and maintain CI/CD workflows for ML projects.
• Monitor production ML systems for performance and errors.

Skills

Rust

Go

C/C++

CI/CD

Cloud

Docker

Kubernetes

Git

Monitoring

Job description

Optimove is a global marketing tech company, recognized as a Leader by Forrester and a Challenger by Gartner. We work with some of the world's most exciting brands, such as Sephora, Staples, and Entain, who love our thought-provoking combination of art and science. With a strong product, a proven business, and the DNA of a vibrant, fast-growing startup, we're on the cusp of our next growth spurt. It's the perfect time to join our team of ~500 thinkers and doers across NYC, LDN, TLV, and other locations, where 2 of every 3 managers were promoted from within. Growing your career with Optimove is basically guaranteed.

Based in Dundee, Scotland, our R&D operation is a dynamic environment, where every developer can impact the flow of technology – from introducing the smallest library to making big infrastructure changes. We welcome open-minded developers who like to share knowledge and help each other to push Optimove forward using the cutting edge of today's tech.

The new MLOps team will be responsible for the seamless deployment, monitoring, and maintenance of machine learning models in production. Acting as the critical link between the data science and R&D teams, this team will ensure that ML models transition smoothly from development to production, maintaining high availability, scalability, and performance.

Key responsibilities include:

• Managing and optimising existing ML model deployments to ensure reliability and efficiency.
• Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management.
• Collaborating closely with data scientists to understand and implement model requirements.
• Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems.
• Implementing robust CI/CD pipelines, monitoring systems, and infrastructure automation.
• Upholding best practices in security, cost management, and infrastructure design for cloud environments.
  
  

This team will play a pivotal role in ensuring that ML initiatives drive value effectively while maintaining operational excellence and we're looking for a Senior Software Engineer to be part of it!

Responsibilities:

• Architect and develop robust pipelines for ML model training, testing, and deployment.
• Implement and maintain CI/CD workflows for ML projects.
• Monitor production ML systems for performance, errors, and drift.
• Automate infrastructure provisioning and deployment using IaC tools.
• Collaborate with team leader to define technical strategies.
  
  

Requirements:

• 4+ years of experience in backend engineering, systems programming, or high-performance software development roles.
• Strong proficiency in low-level programming languages: Rust, Go, or C/C++ (at least one required, multiple preferred).
• Experience building high-performance, scalable backend systems and APIs.
• Knowledge of systems programming concepts: memory management, concurrency, performance optimization.
• Familiarity with ML system architecture and computational requirements (model serving, training infrastructure, data processing pipelines).
• Experience with cloud platforms (AWS preferred) and distributed systems.
• Proficiency with containerization (Docker) and orchestration tools (Kubernetes).
• Strong experience with version control systems (Git) and CI/CD pipelines.
• Understanding of database systems and data pipeline architectures.
• Ability to troubleshoot and optimize complex production systems under load.
• Experience with monitoring, observability, and performance profiling tools.
• Strong communication and collaboration skills for working with ML researchers and data scientists.
  
  

Nice to have:

• Python experience for interfacing with ML frameworks
• Experience with real-time systems or low-latency applications
• Knowledge of GPU computing and CUDA
• Background in numerical computing or scientific software