Interested in this AI/ML Engineer role at Münchener Rückversicherungs-Gesellschaft?
Apply Now →Skills & Technologies
About This Role
Standort Boston, USA Art der Arbeit Vollzeit Arbeitsform Hybrid Job level Führungskräfte Job ID 5776228003 Unternehmen ERGO NEXT Anstellungsart Unbefristet Fachbereich Betriebs und Produktmanagement
Job speichern
Jetzt bewerben
Location: Waltham, MA (hybrid)
ERGO NEXT's mission is to help entrepreneurs thrive. We’re doing that by building the only technology\-led, full\-stack provider of small business insurance in the industry, taking on the entire value chain and transforming the customer experience.
Simply put, wherever you find small businesses, you’ll find ERGO NEXT.
Since 2016, we’ve helped hundreds of thousands of small business customers across the United States get fast, customized and affordable coverage. We’re backed by industry leaders in insurance and tech, and we still have room to grow — that’s where you come in.
Seize the opportunity to be a part of an exciting transition in the insurance industry! We're inviting an accomplished and forward\-thinking data manager to join our AI Underwriting team. This pivotal role will have you innovating and developing groundbreaking digital underwriting features in partnership with our insurance, data, engineering, and machine learning teams.
#### What You'll Do:
- Manage a team of analysts, including training and guidance, establishing clear goals and responsibilities, and ongoing performance management
- Manage the overall budget for underwriting third\-party data.
- Deliver substantial savings by identifying efficiencies and ways to in\-house data services.
- Own and drive the product roadmap for data integrations.
- Serve as the key business connection for external data vendors.
- Partner with the procurement team on vendor contract negotiations.
- Collaborate with internal stakeholders to identify and onboard new external data vendors.
- Manage the Proof of Concept process for testing and evaluating new data vendors.
- Create detailed product requirements for services that leverage data integrations.
- Define success criteria for Proofs of Concept and evaluate results in partnership with underwriting.
- Prioritize PoCs and provide regular reporting on progress and outcomes to senior leadership.
- Oversee vendor performance metrics and usage reporting, using results to inform strategic decisions.
Escalate critical vendor issues and maintain high\-level relationships with vendor leadership.
*
#### What We Need:
- Budget Oversight: Proven experience in managing and tracking a data budget, including forecasting, monitoring monthly expenditures, and identifying areas to optimize spending.
- Vendor Negotiation: Strong negotiation skills to secure favorable terms with data vendors, ensuring cost\-effective solutions that meet business needs.
- Relationship Management: Ability to build and maintain strong relationships with external data vendors, ensuring smooth collaboration and service delivery.
- API Proficiency: Proficient in using API clients such as Postman to test, run, and troubleshoot API services.
- Technical Documentation: Ability to read, understand, and apply API documentation for integration purposes.
- Service Evaluation: Capability to assess new data services, including running Proof of Concepts (PoCs) to determine their fit for the company’s needs.
- Roadmap Development: Experience in developing and maintaining a roadmap for data integrations, aligning with the company’s strategic goals and underwriting needs.
- Executive Communication: Strong presentation skills to effectively communicate the integration roadmap and progress to executives and other senior stakeholders.
- Cross\-Functional Collaboration: Ability to work closely with internal stakeholders, including product, engineering, and underwriting teams, to ensure successful implementation of the roadmap.
- Data Analysis: Ability to interpret vendor data using SQL or Python to validate performance and support strategic decisions.
*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:
$142,000 \- $175,000
Salary Context
This $142K-$175K range is below the median 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 Münchener Rückversicherungs-Gesellschaft, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($158K) sits 13% below the category median. Disclosed range: $142K to $175K.
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.
Münchener Rückversicherungs-Gesellschaft AI Hiring
Münchener Rückversicherungs-Gesellschaft has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $175K - $175K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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 3,823 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.