Interested in this AI/ML Engineer role at Openly?
Apply Now →About This Role
Location
Remote - United States
Employment Type
Full time
Location Type
Remote
Department
Insurance Data Science
Compensation
- Budgeted Salary RangeBelow is the budgeted salary range for this position. Actual compensation for this position will be determined based on the successful candidate's experience and skills. We are committed to providing a compensation package that not only reflects the responsibilities and requirements of the role, but also the unique expertise that the chosen candidate will bring to our team. $285K – $350K • Offers Equity • Offers Bonus
- Full Salary RangeThe full salary range shows the min to max salary range for this position. Actual compensation will be commensurate with experience and qualifications and determined based on various factors including the candidate's qualifications, skills, and experience. $247.5K – $412.5K • Offers Equity • Offers Bonus
Ready for your next chapter?
Build the future of insurance with a team that values simplicity, empathy, and innovation.
Openly is redefining the insurance experience, replacing decades of complexity with clarity and modern technology. We’re the insurance provider people trust and the partner independent agents choose.
What Sets Us Apart
- Technology that delivers: We use data to modernize underwriting and deliver tailored insurance solutions that fit how people actually live.
- Superior protection: Highly customizable coverage designed around what policyholders need.
- Effortless from quote to claim: Simple, intuitive workflows for independent agents and fast, human-centered care for policyholders.
Our Core Values
Our people are as important as our product. Collaboration, open communication, and work-life balance aren't just nice-to-haves, they’re at the core of how we operate.
We look for people driven by integrity, empathy, teamwork, curiosity, and urgency, because a great company starts with the right people.
Diversity, Equity, & Inclusion
We embrace individuality because everyone deserves to work somewhere they belong. We’re committed to building an inclusive culture that spans race, gender, age, religion, identity, and background.
Job Details
As VP, Insurance Data Science, you'll set the strategic direction for how data science is applied across our insurance product — spanning the cost and demand modeling functions that shape our pricing and underwriting algorithms, and growth levers — while managing and developing the people who execute that vision. This is a role for a leader who is technically deep enough to set a high bar, people-focused enough to build a team that clears it, and commercially minded enough to connect modeling decisions to business outcomes. You'll report directly to the Chief Insurance Product Officer and serve as a key voice in how Openly builds and deploys its analytical edge.
Responsibilities
Strategy & Leadership
- Own the vision and delivery of Openly's data science capabilities — prioritizing initiatives that drive pricing precision, underwriting performance, and profitable growth.
- Partner with pricing, underwriting, product, and engineering leadership to ensure data science efforts are tightly integrated with business strategy and operational workflows.
- Serve as Openly's insurance product authority on data science methodology — staying current on advances in ML, AI, and insurance-specific modeling practice and bringing the right innovations to the team with rigor and judgment.
- Represent Insurance Data Science in senior leadership conversations, translating complex modeling work into clear implications for business performance.
Modeling & Research
- Set the technical standard for Openly's cost and demand modeling suite — including GLMs, gradient boosting, neural approaches, and other ML methods — ensuring models are well-validated, production-ready, and coherent with actuarial frameworks.
- Drive the modeling strategy for new initiatives across existing and expansion states, spanning both homeowners and personal auto product lines, ensuring data science capabilities scale with the business.
- Govern the model lifecycle — from development through validation, deployment, monitoring, and refresh — establishing clear standards for when models are production-ready and when they need to be revisited.
- Collaborate closely with actuaries to ensure model outputs can be operationalized within regulatory and pricing frameworks.
- Advance Openly’s data science capabilities through collaborative strategic research with Data/Machine Learning Engineering
Engineering & Standards
- Collaborate with engineering dept leadership on shared development standards — code quality, version control, peer review, testing, reproducibility, and documentation.
- Ensure the Insurance Data Science team can operate at the right pace and scale through strategic investments in infrastructure, tooling, and access.
- Actively partner with Data/ML Engineering to design, prioritize, and implement capabilities critical to scale
- Evaluate and introduce new tools, platforms, and data sources with a bias toward practical impact over novelty.
People Leadership
- Lead, expand, and develop the Insurance Data Science team — including senior data scientists, managers, and analysts — setting clear role expectations, investing in career growth, supporting the recruiting and hiring of external talent, and building a culture of technical excellence and intellectual rigor.
- Own hiring and team structure decisions in partnership with the CIPO, building the capabilities Openly needs now and over the next several years.
- Mentor senior individual contributors and managers, developing the next generation of data science leadership within the organization.
Key Requirements
- 15+ years of experience in data science or a closely related quantitative/actuarial field, with at least 8 years in a leadership role managing highly analytical teams.
- Deep technical fluency in predictive modeling — GLMs, gradient boosting, and modern ML methods — with demonstrated experience delivering production models in a regulated, data-intensive industry.
- Proven experience across both cost modeling (loss ratio, frequency, severity, pure premium) and demand modeling (conversion, retention, price elasticity), and the strategic judgment to prioritize across a portfolio of modeling initiatives.
- Extensive experience in personal lines insurance with models directly driving pricing and underwriting decisions; familiarity with the actuarial context in which data science operates is a strong differentiator.
- Track record of building and leading high-performing data science teams — not just managing delivery, but developing people, raising the technical bar, and creating an environment where rigorous analytical work thrives.
- Strong communicator and cross-functional collaborator, equally fluent in technical depth with data scientists and in strategic implications with executive leadership.
- Commercially minded: you understand how modeling decisions translate to loss ratios, growth, and profitability, and you manage your team's work accordingly.
- Comfortable operating at a growth-stage company where strategic clarity and operational ambiguity coexist — and capable of creating structure without waiting for it to be handed to you.
Perks & Benefits
Great work happens when people feel supported. Our benefits are designed to care for you and your family at every stage of life.
- Remote-first culture: We supported remote work long before it became standard, and we remain dedicated to a flexible work model.
- Competitive pay & equity: Top-tier base salary paired with ownership in Openly’s growth.
- Comprehensive health coverage: Medical, dental, and vision plans tailored to you and your family.
- Life & disability insurance: Full coverage including voluntary supplemental options.
- Paid parental leave: Up to 8 weeks (320 hours) of paid parental leave for eligible employees (with additional disability coverage available for birthing parents).
- 401(k) company contribution: Openly contributes 3% of your gross income, regardless of whether you make personal contributions.
- Home office setup: A $1,500 upfront allowance to build your ideal remote workspace, plus a $75 / month internet stipend.
- Professional development: $2,000 annually to invest in courses, conferences, or resources to help you grow.
- "Be Well" program: A $50 / month stipend dedicated to your personal health and wellness.
- Volunteer hours & referral rewards: Paid time off to give back to your community, plus generous financial rewards for referring great talent.
Note: Select roles may also be eligible for cash incentives or sales commissions. Eligibility for all compensation and benefits is governed by applicable plan terms.
Our Hiring Commitment
Our process is designed to be transparent, respectful, and human. At every step, you’ll get to showcase your strengths, ask tough questions, and find out if Openly is the right fit.
U.S. Citizens, Green Card Holders, and those authorized to work in the U.S. for any employer and currently residing in the US will be considered.
Openly is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry, ethnicity, disability, veteran status, genetic information, sexual orientation, marital status, or any characteristic protected under applicable law. Openly is an E-Verify Employer in the United States. Openly will make reasonable accommodations for qualified individuals with known disabilities under applicable law.
We strive to provide an exceptional applicant and candidate journey when you engage with us. In an effort to respond to applicants in a timely manner, we leverage AI to organize applications and resumes based on required and applicable skills and experience. To allow our applicants to drive their initial interview experience with us, we may leverage an AI-supported scheduling tool so you can choose when to meet with our team. While AI assists with efficiency, all hiring decisions are made by our team members. Rest assured, your data is protected according to privacy laws and company policies. Contact our recruitment team with any questions about our AI-assisted hiring process.
Compensation Range: $247.5K - $412.5K
Salary Context
This $247K-$412K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Openly, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($330K) sits 56% above the category median. Disclosed range: $247K to $412K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Openly AI Hiring
Openly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $412K - $412K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.