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### Job Title: Senior AI Solutions Engineer
Company: Snapsheet
Job Location: Remote
Job Type: Full\-Time
About Snapsheet: Snapsheet is claims technology the way it should be: purposeful, precise, and designed to deliver outcomes. Where others bolt things on, we engineer them into our core systems and processes across cloud\-based claims management, virtual vehicle appraisals, and elite loss and recovery services. Trusted by over 170\+ P\&C Carriers, MGAs, MGUs, TPAs, and logistics companies, our open architecture is built to fit how our companies work, not the other way around.
What you’ll get:
- Remote working environment \- your new commute is however long it takes to walk to your desk!
- Flexibility \- empathy is ingrained in who we are and we are happy to offer a flexible PTO policy, casual dress code, and more!
- Development \- Mentorship programs, 1\-on\-1 management, promote when ready culture, quarterly internal promotion opportunities, and goal setting sessions.
- Fun \- Celebrations just because, yearly in\-person and remote events, Snapsheet Swag, Employee Resource Groups, and more!
Job Overview
As a Solutions Architect at Snapsheet, you are a hybrid powerhouse: a technical builder, a strategic support for our customers, and a dedicated product liaison. You will be completely immersed in our Claim Platform, architecting end\-to\-end solutions that utilize our core infrastructure—with a heavy emphasis on our cutting\-edge AI capabilities. You understand the unique operational challenges of P\&C insurance and will leverage our custom AI Agents, workflow automation engine, and open architecture to make adjusters' lives simpler and claims processing faster.
Equally, you will act as a trusted advisor and the "glue" between Snapsheet and our clients. Meeting with customers weekly, you will extract business needs, unblock AI configuration and prompting challenges, and collaborate on bespoke solutions. As the vital link between our customers, engineering, and support teams, you will champion feature requests and ensure we are always delivering best\-in\-class, AI\-driven outcomes that transform the end\-to\-end claims process.
Responsibilities
- AI Configuration:
Design, prompt, and configure custom AI Agents to seamlessly integrate into the claims process. This includes co\-pilot for adjuster guidance, Photos \& Documents analysis (data extraction, summarization, automation), and designing code\-less AI Agent workflows.
- Architect and Build:
Proactively develop the industry’s best operational flows, utilizing Snapsheet’s Claim Platform and the most advanced AI and automation tools, developing objective metrics to show impact and ROI, and help customers adopt these new processes across their organizations.
- Client Advisement:
Serve in a strategic, account management style capacity, leading weekly client meetings to understand operational pain points, desired outcomes, and roadmap priorities.
- Technical Unblocking:
Proactively unblock customer bottlenecks related to AI configurations, prompting techniques, and system parameters (e.g., model settings, context window data).
- Cross\-Functional Liaison:
Act as the primary conduit between the client, engineering, and support teams. Translate customer needs into actionable feature requests and serve as the first line of technical support for the engineering team.
- Consultative Strategy:
Make expert recommendations on system configurations and AI utilization to drive value, automation, and efficiency for the customer's claims organization.
- Continuous Improvement:
Provide internal feedback on existing AI features and actively contribute to the design and implementation of new capabilities.
Qualifications
- Bachelor's Degree in a relevant field.
- 3\-5\+ years of relevant experience working for or with P\&C claim organizations.
- Strong understanding of P\&C claims processes from an operational level.
- Experience in a B2B client\-facing or account management role, taking a consultative approach to client success.
- Highly technical aptitude with a deep curiosity for AI, Large Language Models (LLMs), and workflow automation.
- Proven ability to act as a bridge between highly technical engineering teams and non\-technical stakeholders.
- Critical thinker, self\-starter, and highly organized in managing multiple priorities simultaneously.
- Excellent written and verbal communication skills.
Preferred Qualifications
- Hands\-on experience implementing AI solutions within insurance or claims operational environments.
- Direct experience with AI prompting, configuring system prompts, and managing context windows.
- Experience configuring SaaS platforms, complex workflow engines, or CRM architectures.
We’re Built to Grow With You – And That Starts With How We Support
You At Snapsheet, we know that growth doesn’t happen in a vacuum—it’s fueled by the right support at the right time. That’s why we’ve built a benefits experience designed to grow with you, wherever life takes you.
- Choose from 2 robust medical plans through Blue Cross Blue Shield—plus, we contribute to your HSA when you enroll in our high\-deductible health plan.
- Offer two dental plans and one vision plan to keep you and your family healthy.
- Peace of mind with company\-paid Short Term Disability, Long Term Disability, and Life Insurance.
- Additional protection through voluntary benefits like Accident Insurance, Hospital Indemnity, Critical Illness, and Legal Assistance.
- 401(k) with a 4% company match—because your future is worth investing in.
- Employee Assistance Program (EAP) with 6 sessions per life incident to support your mental well\-being.
Perks That Make Growing Here Even Better:
- Flexible PTO and 7\.5 company\-observed holidays to recharge on your terms.
- In\-person connection points throughout the year including our annual Summit and Roadshows.
- Snapsheet swag SWAG and surprise mailers to keep the spirit alive.
- Endless opportunity to shape your path—career growth, learning, and real impact are all within reach.
- Health and wellness campaigns that evolve with you year over year.
Compensation that Grows with You
For this position, the base salary range is $95,000 \- $125,000\. While this range serves as a guideline, your actual compensation will reflect your experience and skillset. At Snapsheet, we believe growth should be rewarded—our compensation and benefits are built to evolve with you as your career does.
*This role will be remote based in the U.S. but is not eligible for hire in CA or NY.* *Please note that we are unable to sponsor applicants for work visas for this position at this time.*
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply for jobs unless they meet every single qualification. At Snapsheet, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role but your experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Snapsheet is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need assistance or accommodations, please let us know by emailing [email protected].
Snapsheet is proud to be an Equal Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.
\#LI\-REMOTE
\#BI\-REMOTE Snapsheet is an equal opportunity employer.
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Salary Context
This $95K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Snapsheet, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($110K) sits 39% below the category median. Disclosed range: $95K to $125K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Snapsheet AI Hiring
Snapsheet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $125K - $125K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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