Note: The job is a remote job and is open to candidates in USA. Accelerant is a data-driven risk exchange connecting underwriters of specialty insurance risk with risk capital providers across more than 20 countries and 250 specialty products. The Principal Machine Learning Engineer will own the production machine learning platform, including data and feature pipelines, training and inference services, deployment, monitoring, governance, and infrastructure for agentic AI. The role will also lead a small engineering function, design cross-team integrations, and ensure the reliability, cost, and performance of ML workloads.
Responsibilities
- 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, working directly with those teams to get it right
- Making deployment routine rather than eventful. Versioning, staged rollout, rollback, and CI/CD for models and agents
- Building monitoring that separates data drift from pipeline breakage from genuine performance decay, and that stays informative when labels are delayed
- 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, from overnight batch scoring to low-latency services
- Building model governance and audit trails that satisfy regulators and internal risk committees without becoming a tax on design or delivery
- Leading and growing the function. Setting technical standards, coaching a small team, and partnering closely with the data scientists who depend on your work
Skills
- Substantial experience running machine learning systems in production, including everything that happens after launch
- Strong engineering foundations. Python, infrastructure as code, containers and orchestration, and depth in at least one major cloud provider with sound instincts about cost and failure modes
- Data engineering capability, pipelines, orchestration, storage and access patterns, and enough SQL to hold your own in a warehouse
- A track record of integrating systems across organisational boundaries, including the part where you have to influence teams you don't manage
- Enough statistical literacy to have a real conversation with a data scientist about whether a model is working, and to stay skeptical when the dashboards say it is
- Experience leading or coaching engineers, plus judgement about which infrastructure will pay for itself and which is merely satisfying to build
- Willingness to work with LLMs and agentic AI as everyday tools, whatever your background is today
- The communication skills and credibility to be the person who says a system isn't ready
- Building infrastructure for LLM and agentic systems, including serving, orchestration, retrieval, caching, and keeping spend and latency under control at scale
- Regulated industries where model governance, explainability, and audit trails are requirements rather than aspirations
- Insurance or financial services, whether pricing, underwriting, claims, or portfolio management
- Having worked as a data scientist or predictive modeller at some point, or otherwise being fluent in how models are built and not only how they're shipped
- Internal platforms and tooling that other technical teams genuinely adopted, and a clear view of why they adopted them
- Real-time or streaming systems, feature stores, or high-throughput scoring
Benefits
- Ownership of a function, the freedom to decide how it works, and a team of strong data scientists who need what you build
- A lean, senior team with low bureaucracy and high autonomy
- A collaborative group of people who enjoy solving difficult problems together
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