Interested in this AI/ML Engineer role at Coterie Insurance?
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About This Role
Who we are:
Through a partnership\-based approach, Coterie helps insurance professionals unlock untapped revenue in the small commercial space. With an innovative quoting platform that delivers accurate pricing and bindable quotes in less than one minute, Coterie makes small business insurance effortless.
We are on a mission to build and foster a world\-class team to bring speed, simplicity, and service to commercial insurance. We value integrity, humility, passion, and intelligence. If you want to push yourself and reshape a $200B\+ market, we’re excited to talk to you!
Position Summary:
The AI Engineer will work within the Research \& Development (R\&D) subdomain of Coterie’s broader Data and Analytics (DnA) department to build, test and deploy intelligent systems that enhance business operations, customer and product capabilities. This role combines applied machine learning, software engineering and responsible AI practices to deliver reliable, scalable and secure AI\-enabled solutions in production environments.
Key Responsibilities:
- Partner with DnA teammates, software engineers and business stakeholders to deliver high\-impact, AI\-enabled solutions
- Integrate AI capabilities into internal tools, customer\-facing products and operational workflows using APIs, cloud services and modern software development practices
- Implement secure and compliant AI systems that align with company policies, regulatory expectations and responsible AI principles
- Productionalize AI and machine learning solutions including generative AI, natural language processing, predictive modeling and automation workflows
- Evaluate model performance, reliability, safety, bias, latency, cost and business impact; help establish monitoring and feedback loops for continuous improvement
- Assist with the documentation of technical decisions, system architectures and model assumptions
- Stay current on emerging AI tools, frameworks, model capabilities and industry best practices; recommend and implement pragmatic adoption strategies
Required Qualifications:
- Bachelor’s degree or equivalent professional experience in Artificial Intelligence, Computer Science, Data Science, Economics, Engineering, Machine Learning, Mathematics, Operations Research, Statistics or a related quantitative field
- Intellectual curiosity
- Hands\-on experience building machine learning or AI\-powered applications
- Hands\-on experience working with large language models, prompt engineering, embeddings, vector databases, retrieval\-augmented generation or agentic workflows
- Strong Python proficiency including hands\-on experience with Pytest, Pydantic and FastAPI
- Strong software engineering and familiarity with production\-grade development practices including documentation, testing, version control, code review, CI/CD and observability
- Familiarity with tools/technologies such as AWS/Azure/GCP, Claude, Databricks, Docker, Gemini, Kubernetes, LangChain, LangGraph, OpenAI and SQL
- Working knowledge of data privacy, security, access controls and responsible AI considerations
- A bias toward practical, measurable outcomes and the ability to iterate quickly while maintaining high engineering standards
Preferred Qualifications (bonus experience):
- Advanced degree or equivalent professional experience in Artificial Intelligence, Computer Science, Data Science, Economics, Engineering, Machine Learning, Mathematics, Operations Research, Statistics or a related quantitative field
- Experience working in regulated industries such as banking, financial services, healthcare, insurance or related environments with meaningful audit, compliance, governance, and risk expectations
- Experience building/leveraging reusable AI services or shared tooling that increases operational efficiencies
- A clear point of view on the trade\-offs between Anthropic, Google and OpenAI models across different task types
- Hands\-on experience acquiring data from the web at scale including scraping, proxy management and working with public APIs, aggregators and open\-source datasets
Our interview process:
Our hiring process generally consists of 5 phases. The goal is to provide an opportunity for us to learn more about our candidates while allowing them to get to know us as well!
- Phase 1: Due to the high volume of resumes received, the initial phase of our review process will involve selected candidates completing a pre\-screen coding exercise. Results from this exercise will help determine advancement to subsequent stages of consideration.
- Phase 2: Qualified candidates will first meet with a member of our People Operations team for a phone interview. This discussion is a high\-level conversation to understand more about your background and interests and for us to share more about Coterie and the position.
- Phase 3: Selected candidates will be invited to participate in an experiential exercise interview. This will include a project provided in advance along with a 1\.5\-hour interview conducted with members of our Data Science team.
- Phase 4: Top candidates will then be invited to meet with additional members of our Data \& Analytics (DnA) team for a total of 45 minutes to 1 hour.
- Phase 5: Final candidates will be invited to a 30‑minute interview with a senior member of our leadership team.
What's in it for you:
Coterie has excellent benefits for all full\-time employees. We offer the following:
- 100% remote
- Health insurance through Aetna (we pay 100% of premiums)
- Dental and vision insurance through Guardian (we pay 100% of premiums)
- Basic life insurance (we pay 100% of premiums)
- Access to flexible spending account (FSA) or health savings account (HSA) (for those using HSA eligible plans)
- 401K plan (up 4% match with immediate vest). Must be 21 years of age or older to participate
- Flexible PTO policy offering employees up to 4 weeks of PTO in their first 12 months. Thereafter, PTO usage aligns with company standards and typically does not exceed 5 weeks per calendar year.
- 12 company\-paid holidays each year
- Continuing education annual stipend
- This role is open to candidates at multiple levels. Final leveling (senior or non‑senior) and compensation will be determined based on qualifications and experience. The estimated annual salary range for this position is $115,000–$145,000, based on national market data. Candidates who meet all minimum requirements and demonstrate additional relevant experience, as outlined in the job description, may be considered for compensation above the midpoint of this range. Compensation decisions are guided by internal equity, established salary bands, market data, and the candidate’s skills, prior relevant experience, education, certifications, and overall qualifications.
Work Authorization:
At this time, Coterie Insurance is unable to consider candidates who require current or future visa sponsorship. Applicants must have authorization to work in the United States without the need for sponsorship now or in the future. Falsification of an application, including work authorization status, is immediate grounds for dismissal from consideration.
Salary Context
This $115K-$145K range is in the lower quartile 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 Coterie Insurance, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $115K to $145K.
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.
Coterie Insurance AI Hiring
Coterie Insurance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $145K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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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