Interested in this AI/ML Engineer role at Palomar Holdings, Inc.?
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About This Role
Why choose Palomar?
Palomar is a rapidly growing and innovative insurer focused on providing specialty insurance to residential and commercial customers. Palomar welcomes the best and brightest to our thriving team. Guided by an entrepreneurial spirit, Palomar is building a dynamic and diverse team.
Join a company built on a culture of agility, innovation and authenticity that delivers peace of mind and protection to individuals and businesses impacted by adversity.
Want to learn more about our exciting journey? https://ir.plmr.com/corporate-profile/default.aspx
We are Hiring!
Location: La Jolla, CA
The Details:
Palomar is a fast-growing specialty insurer with an executive mandate for AI. We are hiring a Principal AI Scientist to shape how AI is applied, scaled and enabled across the company, combining scientific depth, hands-on engineering and business judgment.
You will report to the EVP, Head of AI, write code, own AI architecture and standards, and help shape the team as the program grows. Your initial focus is the foundation everything else runs on: how Palomar’s knowledge is represented so agents can use it, a governed library of skills, plugins and connectors that turns a chat tool into how the company works, and a community of employees who build safely on it. You will set the scientific bar for how we evaluate retrieval, test agents and measure outcomes.
As internal owners are trained to take on the platform’s day-to-day operation, your applied science expands into predictive modeling, machine learning and strategic decision support for business leaders.
If you have built an AI platform other teams adopted and want to do it again where the outcome matters, this is that job.
What will you do:
Build the foundation
- Own the technical direction and operation of our enterprise AI platform: models, permissions, connectors, reliability and cost.
- Build a governed marketplace of reusable skills and plugins that any employee can contribute to or use within their own permissions.
- Turn Palomar’s procedures and expert know-how into version-controlled context that agents use reliably, connected to trusted data and core systems.
- Set evaluation and release standards: test cases, acceptance criteria, an accountable owner and monitoring for everything that ships.
- Advise AI leadership on which tools to build, buy, improve or retire.
Grow the builders
- Lead enablement through training, office hours and review of employee-built solutions, and develop internal owners for what gets built.
- Work with business experts to capture judgment and exceptions, and turn what works into tested, reusable capabilities.
Advance applied AI and machine learning
- Shape the applied AI agenda with business leaders, prioritizing by value, feasibility, data readiness and risk.
- Develop, validate and deploy predictive models and decision support for problems from prioritization and routing to portfolio and distribution, choosing statistical, machine learning or LLM methods as the problem requires, with clear boundaries for automation and human review.
- Measure decision quality and business impact against real baselines, monitor what ships, and work with Risk and Compliance to keep it within Palomar’s AI policies.
What we are looking for:
- Master’s or PhD in computer science, statistics, machine learning, operations research or a related field, or equivalent experience.
- 8+ years shipping machine learning or AI systems in production, including recent hands-on work with LLM applications: agents, retrieval, tool integration (for example, Model Context Protocol) and evaluation.
- You have built a platform or shared tooling that other teams adopted, and you can show it.
- Strong statistics and experimental design, and the judgment to tell predictive accuracy from business impact.
- Hands-on depth in supervised and unsupervised learning, feature engineering and model validation, including data leakage, uncertainty and drift.
- Strong Python, SQL and software engineering fundamentals: APIs, Git, testing, CI/CD and cloud. This role writes code.
- The ability to frame ambiguous problems with domain experts, explain tradeoffs plainly to executives and engineers, and help non-engineers build with AI effectively.
Additional experience we value:
- Insurance or financial services, especially underwriting, operations or claims.
- Forecasting, optimization, causal inference or decision science.
- Operating an enterprise LLM platform at company scale.
- Model risk management or AI governance in a regulated industry.
We love people who want to:
- Build strong relationships through transparency, trust, and respect for each other.
- Work in an agile environment, with a resilient mindset to find creative solutions to challenges.
- Collaborate and celebrate diverse perspectives, promoting inclusivity and equality.
- Do the right thing with strong conviction and integrity.
- Be bold and innovative to help make an impact.
- Demonstrable track record of working autonomously and driving progress
- Be intellectually curious, think creatively, and pragmatic
If you thrive in a dynamic environment, and like working with happy, authentic, talented people, you'll enjoy a career with Palomar!
What will you get in return?
- Full Suite of Medical Benefits
- Long Term Financial Incentives – STOCK!
- 401k with 3.5% Automatic Fully Vested Employer Contributions
- Competitive PTO + Holiday Program
- Lifestyle Spending Allowance
- Professional Development Assistance
- Career Exploration Opportunities
- Regular company-wide social events (even virtually!)
- To learn more visit us at www.plmr.com
Diversity at Palomar
At Palomar, we are committed to diversity, equality and inclusion in all facets of the organization. We have built an open culture that celebrates the diversity of our workforce, while actively championing equality and inclusion practices that make us a company people want to work with and work for.
We believe that diversity, equality and inclusion yields greater organizational creativity and productivity, which helps us serve our customers and partners more effectively. Delivering on our diversity commitment returns greater value to our shareholders and ultimately makes a positive impact to the communities in which we do business and to the people who live in them
#PalomarPride #Insurancejobs #hiring
Accommodations:
Palomar will consider reasonable accommodations to ensure a qualified applicant with a disability can be considered for the position. Palomar will also consider necessary modifications to the physical work environment, or to the way a job is usually performed, so an individual with a disability can perform the essential functions of the position. Additional accommodations will be considered that enable an employee with a disability to enjoy equal benefits and privileges of employment like those that are enjoyed by other employees without disabilities.
For more information about our company, visit us at www.plmr.com. Palomar is an Equal Opportunity Employer.
No phone calls or agency solicitation please
CCPA Job Applicant:
Pay Range: $175,000–$275,000 + Bonus Potential & Participation in our Competitive Long-Term Incentive (LTI) Program.
The above pay range is for all US-based roles based on function and level, benchmarked against companies of similar size and scope. To be compliant with local legislation as well as provide greater transparency to candidates, we disclose salary ranges on all job postings regardless of the desired job location. We also offer a competitive annual discretionary bonus program and the opportunity for annual long-term incentive stock grants. In addition, we offer a highly competitive benefits package. Final offer amounts are determined by multiple factors, including candidate experience and qualifications, and may vary from the amounts listed above
Salary Context
This $175K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Palomar Holdings, Inc., 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 Required
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($225K) sits 6% above the category median. Disclosed range: $175K to $275K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Palomar Holdings, Inc. AI Hiring
Palomar Holdings, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in La Jolla, CA, US. Compensation range: $275K - $275K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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