Demo

Staff AI / ML Engineer

AACI Group
Boston, MA Full Time
POSTED ON 5/18/2026
AVAILABLE BEFORE 6/16/2026
About AACI Group

AACI Group, Inc. is dedicated to making insurance more accessible and affordable in catastrophe-prone regions. Climate change is increasing the frequency and severity of wildfires and other natural disasters, while carriers are retreating from high-risk markets. Homeowners are being left with fewer and more expensive options. We are addressing this challenge by building AI-native insurance agencies and digital services that combine risk data, loss prevention, and innovative product delivery.

Role Overview

We are seeking a Staff AI/ML Engineer to design and build the machine learning systems at the core of AACI's AI-native insurance platform. This is a senior, hands-on role for an engineer who can own the full ML lifecycle, from data ingestion and feature engineering through model deployment and monitoring, and who is energized by turning messy, real-world insurance data into production systems that directly improve quoting, underwriting, and loss prevention outcomes. Reporting directly to the CEO, this role is foundational to how AACI scales coverage access in catastrophe-prone markets.

Key Responsibilities

  • Own the design, training, evaluation, and deployment of ML models that power carrier appetite scoring, quote prioritization, property risk profiling, and marketing targeting.
  • Build production data and ML pipelines integrating internal indication/declination data, carrier feedback, and third-party sources.
  • Apply modern LLM tooling to structured and unstructured insurance workflows, including property attribute extraction, document parsing, and agentic automation.
  • Partner closely with Product and Operations to ship models into customer-facing and internal tools, and define the metrics that prove they are working.
  • Establish engineering best practices for ML at AACI: experiment tracking, model versioning, monitoring, reproducibility, and data governance.
  • Collaborate with Engineering leadership on cloud-native architecture decisions and with Data Science on analytical foundations; help recruit and mentor future ML and data hires.
  • Ensure ML systems comply with insurance regulations, data security requirements, and ethical standards for automated decisioning.

Qualifications

  • 7 years of software engineering experience, with at least 4 years building and shipping production ML systems.
  • Demonstrated expertise in modern ML stacks (Python, PyTorch or TensorFlow, scikit-learn) and cloud-native infrastructure (GCP preferred; AWS or Azure acceptable).
  • Track record of owning ML features end-to-end, from problem framing through production monitoring, with measurable business impact.
  • Working fluency with LLM tooling and agentic systems (Anthropic, OpenAI, Gemini, or open-source equivalents), including prompt engineering, evals, and structured output patterns.
  • Strong SQL and data modeling skills; comfort working directly with raw, imperfect operational data.
  • Entrepreneurial mindset and comfort operating in a 'build the ship while flying it' environment.
  • Excellent written communication and the judgment to decide when not to use ML.
  • Nice to have: experience in insurance, fintech, real estate, or another regulated data-heavy domain.

Location, Compensation, and Reporting

  • Based in the Greater Boston, MA, or SF Bay Area, CA, with hybrid flexibility. Remote considered for exceptional candidates in the Western U.S.
  • Reports to the CEO
  • Competitive salary
  • Meaningful equity package
  • Benefits include health and dental insurance and 401(k) matching

Why Join Us

This is a rare opportunity to build the ML foundation of a company tackling one of the most urgent challenges of our time: protecting homes and communities from the growing risks of climate change. As Staff AI/ML Engineer, you will define how AACI uses data and models to expand access to coverage in markets that need it most.

Compensation Range: $250K - $300K

Salary : $250,000 - $300,000

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