Principal Machine Learning Engineer

Role Description

We're looking for someone to own how machine learning and AI run in production at Accelerant. You'll lead a small engineering function responsible for the platform our data scientists build on. That covers:

• Data and feature pipelines
• Training and inference services
• Deployment and monitoring
• The infrastructure behind our agentic AI work

You'll set the standards, coach the team, and be accountable for the whole thing staying up.

Much of the value in this role sits at the seams. Our machine learning systems are not an island. They need to exchange data and decisions with:

• The wider Accelerant platform
• Third-party providers
• Systems owned by other engineering teams

Designing those integrations and building the working relationships with the people on the other side of them is closer to the centre of this job than any single piece of infrastructure.

We take the operational side seriously. We care about:

• Reproducibility, knowing which data and code produced any model currently making decisions
• Training and serving computing features consistently
• Monitoring that remains useful even when ground truth on a claims or pricing model is delayed

If these are problems you've lived with rather than read about, we'd like to talk.

You'd be joining with some foundations already in place but without a decade of accumulated legacy to work around. There is meaningful scope to design the solution, and you'll be the person doing it.

What You'll Work On

• Owning the ML platform end to end, from data and feature pipelines through training infrastructure, model registry and lineage, inference services, and the deployment path between them
• Designing and building integrations with the wider Accelerant platform, third-party providers, and systems owned by other teams
• Making deployment routine rather than eventful, including versioning, staged rollout, rollback, and CI/CD for models and agents
• Building monitoring that separates data drift from pipeline breakage and genuine performance decay
• Standing up the infrastructure behind our agentic AI work, including orchestration, tool and API integration, retrieval and caching, and control of cost and latency
• Owning reliability, cost, and performance across ML workloads
• Building model governance and audit trails that satisfy regulators and internal risk committees
• Leading and growing the function, setting technical standards, coaching a small team, and partnering closely with data scientists

Qualifications

• Substantial experience running machine learning systems in production
• Strong engineering foundations: Python, infrastructure as code, containers and orchestration
• Depth in at least one major cloud provider with sound instincts about cost and failure modes
• Data engineering capability, including pipelines, orchestration, storage and access patterns
• Enough SQL to hold your own in a warehouse
• A track record of integrating systems across organizational boundaries
• Statistical literacy to converse with data scientists about model performance
• Experience leading or coaching engineers
• Willingness to work with LLMs and agentic AI as everyday tools
• Strong communication skills and credibility

Bonus Points

• Experience building infrastructure for LLM and agentic systems
• Experience in regulated industries where model governance is essential
• Background in insurance or financial services
• Experience as a data scientist or predictive modeller
• Experience with internal platforms and tooling that were genuinely adopted
• Experience with real-time or streaming systems, feature stores, or high-throughput scoring

Team Context

You'll join a lean, senior team with low bureaucracy and high autonomy, working alongside data scientists, engineers, actuaries, underwriters, and product managers. We're investing heavily in agentic AI as the next evolution of how a quantitative team operates, and this role helps shape that direction rather than inheriting it.

Why Accelerant?

You'll build the AI and ML foundations for a business where models drive real decisions across the insurance value chain at a company wholly bought into leveraging these systems.

• Ownership of a function, the freedom to decide how it works, and a team of strong data scientists
• Problems that span the full range, from overnight batch scoring to low-latency services
• A collaborative group of people who enjoy solving difficult problems together

Applying

Alongside your CV, please include a short note (a paragraph is plenty) telling us about one piece of machine learning infrastructure you would delete, and what you'd do instead.