Principal Machine Learning Engineer

About the Company

Accelerant is a data-driven risk exchange connecting underwriters of specialty insurance risk with risk capital providers. Founded in 2018 by insurance industry executives and technology experts, Accelerant aims to rebuild the way risk is exchanged. The company operates across more than 20 countries and 250 specialty products, with an AM Best A- (Excellent) rating for its insurers. Learn more at www.accelerant.ai.

About the Role

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, covering data and feature pipelines, training and inference services, deployment, monitoring, and the infrastructure behind our agentic AI work. You'll set the standards, coach the team, and ensure the system remains operational.

This role involves bridging gaps between machine learning systems and the wider Accelerant platform, third-party providers, and other engineering teams. You'll design integrations and build working relationships with stakeholders. Operational reliability is key—we prioritize reproducibility, consistent training and serving environments, and addressing the "slow-label problem" where ground truth may arrive months or years after predictions.

You'll join with foundational work already in place but without legacy constraints, giving you meaningful scope to design solutions and shape the platform.

Responsibilities

• Own the ML platform end-to-end, from data and feature pipelines through training infrastructure, model registry and lineage, inference services, and deployment.
• Design and build integrations with the wider Accelerant platform, third-party providers, and systems owned by other teams.
• Make deployment routine with versioning, staged rollout, rollback, and CI/CD for models and agents.
• Build monitoring to distinguish data drift, pipeline breakage, and performance decay, even with delayed labels.
• Stand up infrastructure for agentic AI, including orchestration, tool/API integration, retrieval, caching, and cost/latency control.
• Ensure reliability, cost efficiency, and performance across ML workloads, from batch scoring to low-latency services.
• Implement model governance and audit trails that meet regulatory and internal risk requirements without hindering delivery.
• Lead and grow the function, setting technical standards, coaching the team, and collaborating with data scientists.

Requirements

• Substantial experience running machine learning systems in production, including post-launch operations.
• Strong engineering foundations: Python, infrastructure as code, containers, orchestration, and cloud expertise (with cost and failure awareness).
• Data engineering skills: pipelines, orchestration, storage, access patterns, and proficiency in SQL.
• Track record of integrating systems across organizational boundaries and influencing teams you don’t manage.
• Statistical literacy to assess model performance and skepticism about dashboard metrics.
• Experience leading or coaching engineers and judging which infrastructure investments pay off.
• Willingness to work with LLMs and agentic AI as everyday tools.
• Communication skills and credibility to advocate for system readiness.

Nice to Have

• Infrastructure for LLM/agentic systems: serving, orchestration, retrieval, caching, and scaling cost/latency.
• Experience in regulated industries with model governance, explainability, and audit trails.
• Background in insurance or financial services (pricing, underwriting, claims, or portfolio management).
• Prior work as a data scientist or predictive modeler, or fluency in model development.
• Success building internal platforms adopted by technical teams, with clarity on why they worked.
• Experience with real-time/streaming systems, feature stores, or high-throughput scoring.

Team Context

You'll join a lean, senior team with low bureaucracy and high autonomy, collaborating with data scientists, engineers, actuaries, underwriters, and product managers. The company is investing heavily in agentic AI as the next evolution of quantitative operations, and this role will help shape that direction.

Why Accelerant?

• Build AI/ML foundations for a business where models drive real decisions across the insurance value chain.
• Ownership of a function, with the freedom to design solutions and support a team of strong data scientists.
• Solve diverse problems, from batch scoring to low-latency services to agentic systems, across 20+ countries and 250+ products.
• Work with a collaborative team that enjoys tackling difficult challenges together.