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Actuarial Machine Learning Jobs (NOW HIRING)

Arlo

Machine Learning Engineer

New York, NY · On-site

$180K - $230K/yr

Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale ... You'll also build the tooling that lets our data scientists and actuaries iterate faster than ever.

Arlo

Machine Learning Engineer

New York, NY · On-site

$180K - $230K/yr

Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale ... You'll also build the tooling that lets our data scientists and actuaries iterate faster than ever.

Apex Systems

Machine Learning Engineer

Jersey City, NJ · On-site

Job#: 3049321 Machine Learning Engineer Location: Jersey City, New Jersey (Onsite) Employment Type ... An advanced graduate degree in Engineering, Mathematics, Statistics, Computer Science, Actuarial ...

Mapfre

Manager, Actuarial Modeling Services

Webster, MA · On-site

$100K - $170K/yr

Experience with machine learning techniques including GLMs, gradient boosting, random forests ... Experience leading actuarial or data science professionals and managing complex projects.

MAPFRE

Manager, Actuarial Modeling Services

Webster, MA · On-site

$100K - $170K/yr

Experience with machine learning techniques including GLMs, gradient boosting, random forests ... Experience leading actuarial or data science professionals and managing complex projects.

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Actuarial Machine Learning information

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$22K

$93.5K

$154K

How much do actuarial machine learning jobs pay per year?

As of Sep 11, 2026, the average yearly pay for actuarial machine learning in the United States is $93,525.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $109,500.00 per year, depending on experience, location, and employer.

What is actuarial machine learning?

Actuarial machine learning is the application of machine learning techniques to traditional actuarial tasks, such as risk assessment, pricing, and forecasting in the insurance and financial industries. Actuaries use these advanced algorithms to analyze large datasets, identify patterns, and make more accurate predictions about future events. By leveraging machine learning, actuaries can enhance decision-making, improve efficiency, and uncover insights that may not be evident through traditional statistical methods.

What are the key skills and qualifications needed to thrive as an actuarial machine learning professional?

To thrive in Actuarial Machine Learning, you need a solid foundation in actuarial science, statistics, and advanced mathematics, typically supported by progress toward actuarial credentials and a relevant quantitative degree. Proficiency in programming languages like Python or R, experience with machine learning frameworks (such as TensorFlow or Scikit-learn), and familiarity with actuarial software are often required. Strong problem-solving skills, analytical thinking, and the ability to communicate complex findings clearly are standout soft skills in this field. These competencies enable professionals to design effective predictive models, translate data-driven insights into business value, and support risk management decisions in the insurance and finance sectors.

How do actuarial machine learning professionals typically collaborate with other departments within an organization?

Actuarial Machine Learning professionals frequently work closely with teams such as underwriting, claims, IT, and data engineering. They translate complex analytical findings into actionable insights for business leaders, participate in cross-functional meetings to align predictive models with business objectives, and often partner with software engineers to deploy machine learning models into production. Successful collaboration requires clear communication, adaptability, and a solid understanding of both actuarial principles and advanced analytics techniques. This team-based approach helps ensure that machine learning solutions are both technically sound and aligned with organizational goals.

What is the difference between Actuarial Machine Learning vs Actuarial Analyst?

AspectActuarial Machine LearningActuarial Analyst
Required credentialsActuarial exams, data science skillsActuarial exams, basic statistical knowledge
Work environmentData science teams, tech-focused projectsInsurance companies, risk assessment teams
Industry usageAdvanced modeling, predictive analyticsPricing, reserving, reporting

While both roles involve actuarial principles, Actuarial Machine Learning focuses on applying machine learning techniques to large datasets for predictive modeling, often requiring data science skills. Actuarial Analysts primarily perform traditional actuarial tasks like pricing and reserving using statistical methods. The roles differ in technical complexity and focus but share foundational actuarial credentials.

What cities are hiring for Actuarial Machine Learning jobs?

Cities with the most Actuarial Machine Learning job openings:

What other helpful pages are available for Actuarial Machine Learning?

Other pages related to Actuarial Machine Learning:

Infographic showing various Actuarial Machine Learning job openings in the United States as of September 2026, with employment types broken down into 20% Internship, and 80% Full Time. Highlights an 80% In-person, and 20% Remote job distribution, with an average salary of $93,525 per year, or $45 per hour.

Machine Learning Engineer

New York, NY • On-site

Arlo
Wholesale • 1 - 10 employees
Learn more about Arloexternal

$180K - $230K/yr

Full-time

Re-posted 14 days ago

Job description

Most of what makes American healthcare expensive isn't medical care. It's the machinery wrapped around it: middlemen taking a cut, fraud nobody stops, and billing systems designed to fight over payment instead of deliver care. The result is higher premiums, denied claims, surprise bills, and a system patients increasingly experience as adversarial.
Arlo is rebuilding health insurance for small businesses from first principles: making sure as much of every premium dollar as possible goes to care instead of getting absorbed by the system around it. We do that by identifying fraud earlier, steering members toward higher-quality and lower-cost care, automating operational overhead, and eliminating vendors whose business exists mostly to take a cut.
AI is the foundation that makes this work. We use it across underwriting, operations, clinical programs, and member experience to build an insurer that becomes more efficient as the technology improves.
We're already operating at meaningful scale: profitable, hundreds of millions in premiums, tens of thousands of members covered, and growing quickly through brokers, employers, and partners. Backed by Upfront Ventures, 8VC, and General Catalyst, with a team from Palantir, YC companies, and longtime healthcare operators.
Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale. We're hiring ML Engineer to build and own the infrastructure that powers it - from training models on tens of millions of patients and hundreds of millions of rows of claims data, to serving real-time quotes in seconds against inference-time datasets that run into the trillions of rows. You'll also build the tooling that lets our data scientists and actuaries iterate faster than ever.
This is an ML infrastructure role with real room to do ML and data science. You'll own the platform, but you'll also have the opportunity to work alongside our data scientists and actuaries to test and evaluate your own ideas - not just support theirs.
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
  • Make it as easy as possible for data scientists and actuaries to test new features and ideas.
  • 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.

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