Senior Backend Engineer (MLOps)

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 MLOps team are 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.

We are looking for a Senior Software Engineer to join this team and contribute to operational excellence and value delivery for ML initiatives.

• 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.

• 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.

• 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

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