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About This Role
At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.
Job Description
Allstate is seeking a forward\-thinking Lead Product Engineer to drive innovation in its AI\-powered Observability platform. This role sits at the intersection of AI engineering, observability, and software development, with a strong focus on automation, intelligent insights, and self\-healing systems across hybrid and multi\-cloud environments.
You will lead the design and development of next\-generation observability solutions powered by agentic AI, enabling proactive detection, diagnosis, and remediation of issues. This role emphasizes building scalable platforms, intelligent automation, and developer\-centric tooling that enhance reliability, performance, and operational efficiency.
As a technical leader, you will shape product strategy, drive engineering excellence, and deliver AI\-driven monitoring and automation capabilities that transform how applications and platforms are operated across the enterprise.Key Responsibilities
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### AI \& Intelligent Automation
- Design and build agentic AI solutions for observability, including autonomous agents capable of anomaly detection, root cause analysis, and automated remediation.
- Embed AI/ML capabilities into observability platforms to enable predictive insights, anomaly detection, and intelligent alerting.
- Develop AIOps frameworks that reduce noise, automate workflows, and improve incident response times.
- Leverage LLMs and AI orchestration frameworks to create self\-service diagnostics and operational assistants.
- Architect and implement end\-to\-end observability solutions across logs, metrics, traces, and events.
- Enhance observability platforms (Datadog, Dynatrace, New Relic, AppDynamics, OTEL) with custom integrations, automation, and AI enhancements.
- Build real\-time insights and health analytics to ensure system reliability, scalability, and performance.
- Lead development of scalable, reusable platform components and APIs for observability and automation.
- Build developer\-first tools and frameworks that simplify instrumentation, monitoring, and diagnostics.
- Apply modern software engineering practices including TDD, CI/CD, microservices, and cloud\-native design.
- Develop full\-stack solutions (backend services, dashboards, automation tooling) using Java/Spring Boot, Python, Node.js, and React.
- Drive automation\-first strategies to eliminate manual operations and improve efficiency.
- Build self\-healing systems that automatically detect and remediate issues.
- Lead initiatives in infrastructure\-as\-code, Observability\-as\-code and automated deployment pipelines.
- Engineer highly resilient systems by focusing on performance, scalability, and fault tolerance.
- Define and evolve enterprise observability architecture, incorporating AI and automation as core principles.
- Collaborate with architecture, platform, and security teams to align solutions with enterprise standards.
- Champion standardization, reusable patterns, and platform scalability across the organization.
- Act as a technical leader and mentor, guiding teams in AI, observability, and engineering best practices.
- Partner with cross\-functional teams to deliver integrated, high\-impact platform solutions.
- Communicate technical concepts and strategies effectively to both technical and executive audiences.
- Foster a culture of innovation, automation, and continuous improvement.
Essential Skills
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- 5\+ years of engineering experience building software, platforms, or automation solutions, with a strong emphasis on observability and distributed systems.
- 4\+ years of hands\-on experience with observability platforms (Datadog, Dynatrace, OTEL).
- 3\+ years of software development experience using Java (Spring Boot), Python, Node.js, or React.
- 2\+ years of hands\-on experience building agentic AI systems (autonomous agents, AI workflows, or AI\-native applications).
- Proven ability to design and implement AI\-driven automation and AIOps solutions.
- Experience integrating LLMs, orchestration frameworks, or AI pipelines into production systems.
- Strong experience with Kubernetes and cloud\-native architectures.
- Hands\-on experience in hybrid environments (on\-prem \+ cloud) across Linux and Windows.
- Expertise in API development, event\-driven systems, and microservices architecture.
- Familiarity with CI/CD pipelines, infrastructure\-as\-code, and DevOps practices.
- Strong problem\-solving skills with a focus on automation and innovation.
- Excellent communication skills to articulate complex technical solutions.
- Passion for emerging technologies, particularly AI in observability and operations.
Supervisory Responsibilities
- This job does not have supervisory duties.
(\#LI\-NJ1\)
Skills
Agentic AI, Agentic AI, Agile Methodology, AI Agents, AI Ops, Anomaly Detection, API Development, AppDynamics, Application Monitoring, Application Programming Interface (API), Automation, Automation Solutions, Back\-End Development, Building Architecture, Business, Business Direction, Business Objectives, Business Operations, Business Processes, Business Software, C (Programming Language), Cloud Infrastructure, Cloud Native, Collaboration, Collaborative Development {\+ 65 more}Compensation
Compensation offered for this role is 100,000\.00 \- 170,500\.00 annually and is based on experience and qualifications.
The candidate(s) offered this position will be required to submit to a background investigation.
Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact.
Allstate generally does not sponsor individuals for employment\-based visas for this position.
Effective July 1, 2014, under Indiana House Enrolled Act (HEA) 1242, it is against public policy of the State of Indiana and a discriminatory practice for an employer to discriminate against a prospective employee on the basis of status as a veteran by refusing to employ an applicant on the basis that they are a veteran of the armed forces of the United States, a member of the Indiana National Guard or a member of a reserve component.
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To view the FMLA poster, click “here”. This poster summarizing the major provisions of the Family and Medical Leave Act (FMLA) and telling employees how to file a complaint.
It is the Company’s policy to employ the best qualified individuals available for all jobs. Therefore, any discriminatory action taken on account of an employee’s ancestry, age, color, disability, genetic information, gender, gender identity, gender expression, sexual and reproductive health decision, marital status, medical condition, military or veteran status, national origin, race (include traits historically associated with race, including, but not limited to, hair texture and protective hairstyles), religion (including religious dress), sex, or sexual orientation that adversely affects an employee's terms or conditions of employment is prohibited. This policy applies to all aspects of the employment relationship, including, but not limited to, hiring, training, salary administration, promotion, job assignment, benefits, discipline, and separation of employment.
Allstate provides a comprehensive technology setup, including a laptop, monitors, headset, keyboard, and mouse. Employees eligible to work from home also receive a monthly connectivity reimbursement to help offset internet costs.
When working from home, you must have a dedicated, private workspace free from distractions, along with appropriate desk and seating. Reliable internet is required, with minimum speeds of 50 MB download and 5 MB upload.
Salary Context
This $100K-$170K 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 Allstate 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($135K) sits 38% below the category median. Disclosed range: $100K to $170K.
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.
Allstate Insurance AI Hiring
Allstate Insurance has 10 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Based in Remote, US. Compensation range: $80K - $290K.
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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