Senior II Software Engineer - Machine Learning Platform

Requirements

• You care about bringing value and satisfaction to your customers - the developer/user experience of the people who use your platform matters as much as the technical elegance of the solution
• You think in systems, not just features - you consider how components interact, where complexity lives, and how to reduce it
• You are comfortable working across the stack - from infrastructure and orchestration to APIs and developer tooling
• You take ownership of problems end-to-end, from understanding the need through to production and beyond
• You communicate clearly, build consensus, and enjoy collaborating with people from different disciplines - data scientists, product managers, and fellow engineers
• You have a growth mindset - curious, experimental, and open to giving and receiving regular feedback
• You share your ideas, continuously improve yourself and the team around you, and are comfortable working collaboratively in a hybrid environment
• Strong engineering background in Python with experience building and maintaining production systems
• Experience with Kubernetes - deploying, managing, and troubleshooting containerised workloads
• Familiarity with ML platform tooling such as MLflow, Airflow, or similar orchestration and experiment tracking frameworks
• Experience with cloud infrastructure (AWS or GCP) including compute, storage, and networking
• Understanding of distributed systems principles - you know the trade-offs between different architectures and can make pragmatic decisions
• Experience with observability and monitoring - building dashboards, alerts, and tooling that helps teams understand system health
• Solid understanding of software engineering best practices - testing, code review, CI/CD, and clean, maintainable code
• Ability to use AI-assisted development tools responsibly, while validating outputs and retaining ownership of code quality

What you'll be doing

• Building and maintaining core ML platform services including model serving infrastructure, training pipelines, and experiment tracking
• Contributing to the evolution of our platform from individual service offerings towards a coherent, user-driven product
• Improving platform scalability, reliability, and operability, ensuring our infrastructure can support hundreds of models in production while making pragmatic trade-offs around cost, complexity, and user needs
• Improving observability and monitoring across the model lifecycle, helping data scientists understand model health and performance
• Collaborating with data scientists to understand their workflows, pain points, and needs - treating them as your customers
• Participating in on-call/support rotation, contributing to platform stability and identifying opportunities to reduce operational toil
• Helping shape the technical and product roadmap by contributing to discovery, spikes (exploratory/investigative work), and architectural decisions
• Sharing knowledge across the team, reduce silos, mentor others, and help raise engineering standards through design reviews, code reviews, documentation, and continuous improvement

Nice to haves

• Experience building or contributing to internal developer platforms or self-service tooling
• Familiarity with ML workflows - training, serving, feature engineering, model monitoring (you don't need to be a data scientist, but understanding the domain helps)
• Experience with Infrastructure as Code (Terraform, CDK, or similar)
• Exposure to streaming or batch data processing frameworks (Spark, Flink, Kafka)
• Interest in platform-as-product thinking - treating adoption, user experience, and feedback loops as first-class concerns

Perks and benefits

• The opportunity to shape a platform that directly enables ML-driven decisions across a global financial product serving millions of customers
• A team that values autonomy, experimentation, and continuous improvement - where your ideas about how we work matter as much as what we build
• Real ownership of the systems you work on - from architecture decisions to production operations
• Exposure to complex, real-world ML infrastructure challenges at scale