Interested in this AI/ML Engineer role at Berkshire Hathaway Direct Insurance Company?
Apply Now →Skills & Technologies
About This Role
Description:
We are the team that enables customers to purchase insurance policies direct online through biBERK, which is proud to be a part of Berkshire Hathaway Insurance Group. We support our marketing initiatives by delivering personalized customer experiences and we support our back office by creating efficiencies and continual improvement around their processes.
This role would be part of the R&D team for a fast-growing insurance technology leader. The goal is to help us build artificial intelligence algorithms, which replicate traditional human effort in insurance industry in Underwriting, Pricing, and Premium Audit. The algorithms are built using a variety of tools such as machine learning, logic based coding, utilizing third-party integrations, and modern AI learning tools. The algorithms are aided using our own data, third-party data, and expert input from various departments.
In this role, you will be an integral team with outstanding communication, collaboration, and attention to detail. You will be counted on to build robust models, write functional code, and to work with others to research and develop solutions. We have exciting opportunities for you to innovate, influence, transform, inspire, and grow within Berkshire Hathaway.
This job is hybrid with 2 mandatory in-office days per week at our Stamford, CT location.
Job Responsibilities
- Develop, test, and update Artificial Intelligence algorithms in Python
- Write clean, scalable code using our current technology stack
- Apply Property & Casualty Insurance knowledge to the risk-based AI algorithms
- Participate and contribute with design, research, and technical discussions
- Develop strong partnerships with business teams and other groups
- Demonstrate the ability to test and deploy algorithms and revise, update, debug, and refactor code
- Deliver the required capabilities per Berkshire Hathaway standards
Requirements: Required Qualifications:
- 2 years of professional experience
- 2 years of programming or 2 years of Commercial Property and Casualty Insurance Experience
Preferred Qualifications:
- Bachelor’s degree in Computer Science/Engineering/Related field or equivalent working experience
- Practical working knowledge of Microsoft Azure
- Strong design, coding, debugging and problem-solving skills
- Outstanding communication, collaboration capabilities, and attention to detail
- Understanding of architectural, design, and agile patterns and methodologies
- Advanced Python Knowledge
About Us
biBerk is where commercial insurance buyers can obtain coverage for their businesses from insurers of the Berkshire Hathaway group of Insurance Companies, one of the best capitalized insurance groups in the world. Our ultimate parent, Berkshire Hathaway Inc. (berkshirehathaway.com) is a holding company with diversified interests in a host of industries, including insurance, energy, transportation and manufacturing. Most policies issued through biBerk.com will be underwritten by Berkshire Hathaway Direct Insurance Company ("BHDIC"), which is an AM Best rated A++ insurer.
BHDIC is domiciled in Omaha, Nebraska. BHDIC and the team at biBerk are focused on helping small business owners quickly and easily buy affordable insurance directly from a financially strong insurance company they can trust.
BHDIC is excited to announce our comprehensive benefits package with some new and enhanced features for 2026 that include:
- Medical (PPO/HDHP), vision, disability, and life insurance.
- Enhanced dental plan with orthodontia coverage in addition to a standard plan.
- Generous PTO plan for all benefit-time eligible employees.
- Paid company holidays and 4 floating holidays.
- Paid parental leave.
- Employee Retirement Savings Plan/401(k) with company match and immediate vesting.
- Education Assistance Program that offers 100% upfront tuition reimbursement after 6 months of service for approved degree programs.
- Service Recognition Program that provides a monetary award to be used toward a vacation every 5 years of employment.
- Wellness Initiatives that include Fitness Center and Weight Watchers Reimbursement programs.
- Voluntary benefits that include accident, critical illness, and hospital indemnity.
- Employee discount and rewards program on travel, tickets, electronics, home, and more.
In accordance with pay transparency laws and regulations, the following good faith compensation range estimate is being provided. The salary range for this position is $80,000 - $130,000 per year. Final compensation will be based on candidate qualifications, geographic location, and other considerations permitted by law. This position does not have bonus potential.
Salary Context
This $80K-$130K 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 Berkshire Hathaway Direct Insurance Company, 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 ($105K) sits 50% below the category median. Disclosed range: $80K to $130K.
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.
Berkshire Hathaway Direct Insurance Company AI Hiring
Berkshire Hathaway Direct Insurance Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Stamford, CT, US. Compensation range: $130K - $130K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.