Interested in this AI/ML Engineer role at Assured?
Apply Now →About This Role
Assured is on a mission to modernize insurance. Claims processing (i.e. should we pay this claim?), while often overlooked, is the foundation of the entire industry. It’s currently highly manual, involving phone calls, faxes, and gut instinct, costing tens of billions of dollars a year. We can do better.
At Assured, we provide large insurers with the software solutions they need to win in a modern, technology\-driven world. From self\-service claim\-filing software to backend fraud detection, we’re the engine that powers claims processing for some of the largest insurers in the world.
The challenges we face are deep and diverse, from creating digital experiences that provide comfort and clarity to claimants at their most stressed and vulnerable to orchestrating large\-scale ML\-driven decision\-making on billions of dollars of claims payments, life at Assured is dynamic, collaborative, and rewarding.
We are looking for a Senior Copywriter, AI \& Category Narrative – a writer who is genuinely expert in AI and can make agentic AI, frontier models, and LLM\-powered systems legible, credible, and compelling to the people who run claims at the largest US carriers. Assured sells to the top P\&C carriers that own roughly 98% of the nation's claims volume. The capability and the proof are real; the writing that carries it to the market is the multiplier. You are the hands\-on maker of that writing.
This is a craft\-and\-substance role that complements our Content Strategist: the strategist sets the plan and the calendar; you go deep on the subject matter and write the words. You will turn complex technical capability – how agents reason, what "agentic" actually means in a claims workflow, where frontier models help and where they don't – into thought leadership, explainers, web and product copy, social, and the ghostwritten point of view that builds our category. You write for a skeptical, senior, regulated audience, and you earn their trust because you understand what you're writing about.
You will partner with the Content Strategist, the CEO/founder, and the rest of the Marketing organization, and operate inside carrier compliance norms on everything that leaves the building. If you want to write at the frontier of AI for a category that is being defined in real time – not summarize it from the outside – this is your seat.
What You'll Do...
Be the expert writer on AI – Write authoritatively about frontier AI, models, and LLM\-powered systems for enterprise – copy that holds up with technical readers (i.e. CIOs and CAIOs) and lands with claims executives who are not.
Carry the category narrative – Translate Assured's "frontier AI for claims" point of view into thought leadership, bylines, explainers, and long\-form pieces that the market, press, and analysts come to recognize as ours.
Write across every surface – Produce web and landing copy, product and feature narratives, social posts, newsletters, and sales\-supporting content – in one consistent, credible voice.
Ghostwrite the point of view – Partner on the CEO and founder voice – drafting POV, op\-eds, posts, and talk tracks that make Justin a leading voice in insurance AI.
Make the complex clear – Sit with the product, and PMM teams, understand the capability deeply, and turn it into language that is precise, vivid, and free of hype.
Complement the Content Strategist – Take the strategy and calendar and make it real on the page – owning drafts end to end, from brief to publish\-ready copy.
Own the blog – Write and ship a steady stream of blog posts and articles that turn our AI point of view into search\-visible, shareable content and build Assured's organic authority in the category.
Raise the bar on quality – Set and hold a high editorial standard for accuracy, voice, and clarity across everything the team ships.
Keep it compliant – Write inside carrier anti\-bribery and compliance norms, and check claims and substantiation before anything goes out.
What You Bring...
5–8\+ years as a copywriter or content writer (senior IC to lead level), with a portfolio of published work you wrote yourself.
Genuine subject\-matter command of AI – agentic AI, frontier models, and LLMs for enterprise – demonstrated through writing that technical readers respect.
A track record of writing for B2B, enterprise, or technical audiences and making complex capability clear and persuasive.
Exceptional writing range: thought leadership and long\-form, plus crisp web, product, and social copy in a single coherent voice.
Comfort going deep with product and engineering to learn a hard subject, and the judgment to write about it without hype.
An individual maker who wants to write – not manage a team or run an agency;
Based on the U.S. West Coast.
Insurance, fintech, or another regulated/enterprise domain a plus; if not, genuine enthusiasm to learn the claims world fast.
Experience writing inside an early\-stage or high\-growth startup at a stage similar to Assured's – comfortable with ambiguity, fast iteration, and building from scratch.
High ownership and low ego – you do the thinking and the writing, and you sweat every word.
Benefits:
Competitive Compensation: Competitive salary and equity packages for all employees
Healthcare Plan: Platinum medical, dental, and vision
Free life insurance: Including long\-term disability \& short\-term disability
Unlimited PTO: Uncapped vacation days \& paid holidays
Family Leave: Maternity \& paternity
401(k) Contribution: Assured contributes 3% of your income, even if you don't contribute
WFH Benefits: Lunch on us 2x/week, monthly phone stipend \& other home office perks
Health FSAs \& HSAs: Pre\-tax accounts for out\-of\-pocket medical expenses
Team events \& Offsites: We're remote, but we regularly get together
\*\*We have been made aware of individuals falsely posing as recruiters from Assured Insurance Technologies Inc. Please note that we only contact candidates from official @assured.claims email addresses and all interviews are conducted through verified company channels. If you are unsure whether a message is legitimate, please contact us directly at recruiting\-ops@assured.claims before sharing any personal information\*\*
Our Commitment:
*We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact us to request accommodation.*
Compensation Range: $140K \- $200K
Salary Context
This $140K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Assured, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($170K) sits 22% below the category median. Disclosed range: $140K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Assured AI Hiring
Assured has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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