Machine Learning Engineer

What You'll Work On

• Training infrastructure for underwriting
  • Build and own the infrastructure layer that powers our underwriting model, trained on tens of millions of patients and hundreds of millions of rows of claims data.
  • Make training reliable, reproducible, and scalable as data volume and model complexity grow.
• Real-time inference for quoting
  • Build and own the API layer that produces quotes in seconds — serving a trained model against a much larger inference-time dataset, on the order of trillions of rows of claims across hundreds of millions of people.
  • Own the latency, reliability, and scalability of the serving path the quoting product depends on.
• Accelerate data science iteration
  • Build backtesting and validation infrastructure so model performance can be measured quickly and trustworthily.
  • Remove friction from the path between an idea and a validated, production-ready model — make experimentation simpler than it's ever been.

What We're Looking For

• A strong track record building ML or data infrastructure in production at scale.
• Deep proficiency in Python, with comfort in processing large datasets (Spark, Databricks, or equivalent).
• Experience with model training pipelines and/or low-latency model serving in production.
• Experience building tooling that makes other people faster — feature testing, experiment tracking, backtesting, or similar developer/researcher-facing infrastructure.
• The ability to own systems end-to-end, set standards, and operate reliable production infrastructure (SLAs, monitoring, on-call).
• Genuine interest in the modeling itself — you want to occasionally get your hands into the data science, not only the infrastructure.

Nice to Have

• Prior experience in a regulated space like healthcare or insurance.
• Experience with MLOps tooling (MLflow or similar), feature stores, or experimentation platforms.
• Experience supporting data science or actuarial teams in production environments.

Compensation

$180,000 – $230,000 + equity

Why Join Arlo

• High ownership: You’ll get real responsibility from day one—our high-trust team empowers you to run with big problems and shape core parts of the company.
• Join an important mission: Your work directly influences how people access care and improves lives at scale.
• Growth & expansion: We’re moving fast, and as we grow, your scope will grow with us—new challenges, bigger opportunities, and rapid career velocity.
• Apply AI to a problem that matters: Instead of optimizing ads or cutting labor costs, you’ll use AI to fundamentally reimagine how people get healthcare.
• High pace, high collaboration: We operate with velocity, first-principles thinking, and a team that works closely, openly, and with ambition.

Exact compensation inclusive of salary and any bonuses is determined based on a number of factors including experience and skill level, location, and qualifications which are assessed during the interview process.

Arlo is an equal opportunity employer. We do not discriminate based on age, race, color, creed or religion, national origin, sexual orientation, gender identity or expression, military status, sex, disability, predisposing genetic characteristics, marital status, familial status, status as a victim of domestic violence, or arrest or conviction record, as defined under New York State law.

🔒 Your safety matters to us. If you're selected to move forward in our hiring process, you'll hear directly from a member of our Recruiting team via an @joinarlo.com email address. We will never ask for personal or financial information outside of our formal onboarding process. When in doubt, please reach out to us to verify at: recruiting@joinarlo.com.