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About This Role
If you love owning a system and seeing it run smoother, faster and with better data because of your work, this role is for you. Here, you'll design the workflows our claims team runs on, own the platform behind them, and build the reports and AI tools that guide the company's growth.
At Allied Public Adjusters, our mission is a world where every property loss is settled truthfully and equitably. Since 1997, we've worked on behalf of policyholders to demand what's right by using technical expertise, field investigations, and uncompromising quality. We empower policyholders with the most skillful representation, while ensuring carriers have the information they need to do what's right. Today we're rebuilding how we handle claims around pods: player-coaches leading tiered adjusters, with dedicated estimators and attorneys, all working to one standard.
The Claims Operations Manager is the bridge between our claims team and our technology. Our Director of Claims defines what good looks like and coaches the team. You design and build the systems that make it happen and measure it: the workflows, Claimore (our in-house claims platform), the dashboards and audits, and the AI tools the team works with.
Responsibilities
Design the Workflows
- Design and update the systems our claims team works in for ease of use, depth and accuracy, across every workflow a claim runs through: client onboarding, the client relationship, carriers, vendors, legal, and our in-house estimating and contents team.
- Configure each workflow from the handoff after signing through the settlement profile, with stages, handoffs, triggers and SLA timers, so the team can follow it easily and every step can be tracked.
- Translate stakeholder requests into defined solutions to build or backlog.
Build the Reporting, Dashboards and Audits
- Design, run and maintain the audits that show whether files meet the standard, starting with the 45-day file audit, submissions within 30 days, call and email logging, and file handling.
- Track submissions and how fast they go out, collections against submissions, cycle time, caseload by adjuster tier, and audit scores.
- Turn the KPIs and outcomes the Director of Claims defines into automated scorecards and dashboards, for the department, every pod and every adjuster.
- Own the claims numbers the company reports, so leadership and the All Hands work from one version.
- Pull data from Claimore and our other systems into one view, with Claimore as the source of truth, and keep improving it as claim volume grows.
Claimore Product Owner
- Serve as product owner of Claimore: own the roadmap and the backlog, and decide what our vendor development team builds and in what order.
- Turn stakeholder input into clear requirements, prioritize them against everything else on the backlog, and keep the Director of Claims updated on deadlines and delivery.
- Accept each release before it reaches the team, and check the development team's invoices against delivered work.
Build with AI
- Build the tools yourself: dashboards, automated audits and reports, using AI tools like Claude along with Power BI and Excel. Larger Claimore features go to the development team.
- Build and maintain our Claims AI, the Claude-based knowledge base our claims team works files with. The Director of Claims and the team supply what it knows; you structure it, keep it current and make it part of the team's day.
- Look beyond Claimore for things to automate, from how we order aerial measurements and material testing to how we track collections.
Leadership & Process
- Run a weekly review with the Director of Claims and the Chief Growth Officer, with an agenda and a pre-read: what you shipped, what the audits found, and what's next.
- Change Claimore or a workflow on your own when the change doesn't require retraining. Review and align with the Director of Claims first for any changes that require retraining.
- Review the SOPs the Director of Claims writes, and flag any step the system can't track.
- Spend a week on site each quarter shadowing the teams who use what you build, to see what the data can't show you.
- Grow the function with the pods: past three pods, hire and lead the ops managers who run audits and exceptions for the new ones. Lead well and build something reliable that scales, and the seat can grow into Director of Operations as ops expands beyond claims to growth, finance, people ops, IT and legal.
Requirements
- 5+ years of experience as product owner of a software product, managing the backlog, priorities and releases directly with a development team, in-house or vendor.
- Has designed and run operational workflows, and redesigned a process so the data it produces is complete and reliable.
- Can build dashboards and reports from raw data, in Power BI, SQL, Excel or AI tools like Claude.
- Has designed and run audits, compliance reviews or quality programs on detailed files or processes.
- Actively builds with AI: has made a real tool, workflow, agent or knowledge base with tools like Claude, at work or on their own time.
- Strong project manager who keeps developers and stakeholders on a plan.
- Writes and presents clearly, and runs meetings with an agenda and a pre-read.
- Self-directed and responsive in a remote role, keeping a steady cadence with the people they depend on.
- Experience in or around property insurance claims, with a working knowledge of the claim lifecycle and its terminology is ideal.
- Has lived through a system rollout or data migration and seen what breaks after go-live is ideal.
Results
- Every claim step from signing through settlement is tracked in Claimore, and leadership can see where any file stands without asking an adjuster.
- The claims team works files with the Claims AI every day, and it stays current.
- Audits of claim and legal files run on schedule without manual pulls, and every finding ends as a system fix or a coaching item, never as the same report next month.
- Every adjuster and pod has an automated scorecard, the Director of Claims and the Chief Growth Officer make their weekly decisions from your dashboards, and the company's claims numbers come from one source.
- Claimore has one roadmap and one owner, the development team builds what the standard needs, and every invoice matches delivered work.
- Audit scores and on-time submissions rise quarter over quarter, toward 90% on the 45-day audit.
- Each new pod launches with its workflows, scorecards and dashboards live on day one.
Logistics
- Location: Remote within the Pacific, Central, or Mountain time zones.
- Travel: Your first 30 days in person at our Southern California offices, then one week on site each quarter.
- Schedule: Available during Pacific business hours of 9-6 PST.
Salary
- $140,000 - $160,000 base + 10% bonus
Benefits
- 401(k) with 3% non-elective contribution
- Health and dental insurance options
- Generous paid holidays and paid time-off policy
- Career advancement and professional development opportunities
Our Core Values: We Show Up with G.R.I.T.
- Go-Getters: We pursue truth and fairness with rigor and expertise. We are proactive, responsive, and committed to professional service.
- Relationship Builders: We nurture relationships based on mutual respect, challenging insurers when we must and helping them when we can, all in the spirit of resolving claim matters fairly.
- Intelligent Experts: We work to achieve mastery of our respective crafts, trusting our teammates to do the same. By focusing on core competencies, we create a team stronger than the sum of its parts.
- Truth Champions: We pursue truth above all else. To champion our clients' interests most effectively, we seek precisely what is owed, nothing more, and absolutely nothing less.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Allied Public Adjusters, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Allied Public Adjusters AI Hiring
Allied Public Adjusters has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $160K - $160K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,580 tracked positions.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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