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About This Role
Location Palo Alto, United States Job type Full-Time Work mode Hybrid Job level Professional Job ID 5892638003 Company ERGO NEXT Employment type Regular Area of expertise Finance, Accounting & Controlling
Location: Palo Alto, CA (hybrid)
Small business owners don't have a risk department. They spend their time and energy starting and growing a business. NEXT's mission is to help entrepreneurs thrive, and we're using AI to make business insurance simple, fast, and affordable enough that it never costs them a day of work.
We're an AI technology company that happens to also be a fully licensed insurance carrier. Because we're a full-stack carrier, AI isn't a layer bolted onto someone else's product. It's in the underwriting, the pricing, the classification, and increasingly in how the product itself gets built. We're rebuilding the core judgment loops of a hundreds-of-years old industry, and doing it with a lean team, which is exactly why AI matters.
We underwrite our own risk, build our own systems, and own the entire value chain, so the product decisions made here show up directly in what a customer pays and how fast they get covered. Since 2016 we've insured hundreds of thousands of small businesses across the country. As a subsidiary of Munich Re and ERGO, our growth is backed by the financial strength of one of the world's leading reinsurance and insurance groups.
About the role:
ERGO NEXT is building its Finance organization to be AI-native — starting with reinventing how we forecast the business from the ground up. We're looking for a Strategic Finance Manager, AI & Forecasting to help lead this transformation: someone with strong core strategic finance fundamentals who is equally excited to redesign forecasting, planning, and reporting workflows around AI/LLM-based tools rather than layering AI on top of legacy spreadsheet processes. Reporting to the Head of Revenue & Growth FP&A, this is a high-visibility, high-impact role at the intersection of strategic finance and applied AI, with the mandate to define what "AI-native FP&A" looks like at ERGO NEXT.
What You’ll Do:
- Lead the redesign of the topline revenue forecasting process from the ground up, using AI/LLM-based tools and workflows in place of legacy manual, spreadsheet-driven approaches
- Partner with Finance and Data Science to identify, evaluate, and implement AI tools and agentic workflows that improve the speed, accuracy, and scalability of forecasting and planning
- Drive long-range and annual planning process while building repeatable, AI-assisted modeling and reporting frameworks
- Spearhead strategic finance activities applying AI-driven analysis to accelerate diligence and scenario modeling
- Partner cross-functionally to define and automate KPI tracking, using AI to surface insights and recommendations faster than traditional analysis allows
- Prepare Board materials and executive presentations, incorporating AI-generated analysis and scenario modeling to sharpen the narrative
- Act as an internal champion and educator for AI adoption within Finance, documenting playbooks and mentoring team members as new tools and workflows are rolled out
What We Need:
- BA/BS in finance, accounting, economics, or a quantitative discipline
- 4–6 years of finance experience in a fast-paced environment with a track record of success (background in investment banking, corporate development, or strategic finance a plus)
- Genuine enthusiasm for AI tools (e.g., LLM-based agents, AI-powered modeling/analysis platforms) and a track record of adopting or building workflows around them — prior hands-on use of tools like Claude, ChatGPT, or similar in a finance/analytics context strongly preferred
- Ability to break down and solve complex, ambiguous problems with strong business acumen and data-driven techniques, including comfort redesigning a process rather than just operating an existing one
- Basic technical fluency (e.g., SQL, Python, or low-code/AI tooling) sufficient to prototype or evaluate AI-driven finance workflows a plus, though not required
- Excellent attention to detail and organizational skills, with the ability to manage competing and shifting priorities in a fast-paced environment
- High degree of reliability and integrity, with sound judgment operating in a newly-defined, ambiguous role
- Strong interpersonal and communication skills, with the ability to bring both technical and non-technical stakeholders along on a change-management journey
Note on Fraudulent Recruiting We have become aware that there may be fraudulent recruiting attempts being made by people posing as representatives of ERGO NEXT Insurance. These scams may involve fake job postings, unsolicited emails, or messages claiming to be from our recruiters or hiring managers.
Please note, we do not ask for sensitive information via chat, text, or social media, and any email communications will come from the domain @next-insurance.com or @nextinsurance.com. Additionally, Next Insurance will never ask for payment, fees, or purchases to be made by a job applicant. All applicants are encouraged to apply directly to our open jobs via the careers page on our website. Interviews are generally conducted via Zoom video conference unless the candidate requests other accommodations.
If you believe that you have been the target of an interview/offer scam by someone posing as a representative of Next Insurance, please do not provide any personal or financial information. You can find additional information about this type of scam and report any fraudulent employment offers via the Federal Trade Commission's website (https://consumer.ftc.gov/articles/job-scams), or you can contact your local law enforcement agency. The range displayed on this job posting reflects the minimum and maximum target for new hire base salaries for the position in the location(s) listed. Within the range, individual pay is determined by additional factors, including, without limitation, job-related skills, experience, and relevant education or training. ERGO NEXT employees are also potentially eligible for our annual performance-based incentive program, in addition to our benefits package, consisting of our partially subsidized medical plan, fully subsidized vision/dental options, life insurance, disability insurance, 401(k), flexible paid time off, parental leave and more.
US annual base salary range for this full-time position:
$117,000 - $158,000
Salary Context
This $117K-$158K range is in the lower quartile 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 ERGO NEXT, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $117K to $158K.
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.
ERGO NEXT AI Hiring
ERGO NEXT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US. Compensation range: $158K - $158K.
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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