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About This Role
About the team
Symetra's AI Enablement team is a fast\-growing team of builders inside our Data Analytics organization. Our goal is to accelerate AI adoption across the company in two ways: by building shared platforms, tooling, and patterns that let other teams effectively integrate AI into their workflows, and by shipping AI\-powered applications for the most important use cases.
We work in small, cross\-functional teams, build with the people who use what we make, and move fast. Our first product in production, docstream, is an AI\-powered document\-processing platform that currently supports several of Symetra's back\-end operational processes. We plan to continue to iterate on docstream and to build out other products using the same modular architecture, integrating third\-party products where it makes sense.
About the role
As Symetra's first Developer Advocate, your goal will be to help everyone at the company get more out of AI. You'll work with two primary audiences: business teams adopting tools like ChatGPT and Claude, and engineers getting the most out of AI\-assisted development. You'll run training and office hours, build and share best practices, manage our enterprise AI tooling, and make our tools and capabilities easy to use. When needed, you'll pull in engineers or leaders from our team.
What you will do
- Help business teams use AI tools like ChatGPT and Claude well, through training, onboarding, and support.
- Help engineers get the most from AI\-assisted development, through office hours, best practices, and other guidance.
- Manage our enterprise AI accounts and tooling and make our data and capabilities easy to use (for example, working with engineering teams to set up MCP servers that give AI access to their applications).
- Define what good AI adoption looks like, track it, and double down on what works.
- Create engaging content, written and video, plus the demos and examples that turn AI from a buzzword into something people use (we have a studio in our Bellevue, WA office you can use if you're in the area).
Partner with groups already driving this work (the innovation community, engineering COE, analytics engineering and cloud office hours, and more) on training and joint efforts.
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Who You Are
- An experienced technical communicator (typically 5\+ years across engineering, developer relations, or enablement) who can teach and influence both engineers and business users.
- Hands\-on with AI yourself: fluent with AI coding agents (like Claude Code) and modern AI tools, with real things you've built.
- Enough engineering ability to build integrations and demos (for example with Python and APIs) and earn engineers' trust.
- Strong writing and presentation skills: docs, talks, and office hours.
- A knack for driving adoption and measuring what works.
Why Work at Symetra
Here’s what some of our employees have to say about why they work at Symetra:
*“Working across different departments at Symetra has been a fantastic experience. What truly stands out is how much the leaders genuinely care about every employee, regardless of role. It’s inspiring to be part of a company so deeply focused on its people and committed to making a positive impact in the community.” \- Bill G., Underwriting Consultant“Symetra is truly a great place to work. The positive work climate, strong sense of team, and the resources available make it feel like one cohesive family. What stands out most to me is the company’s deep commitment to diversity, equity, and inclusion—it’s not just a statement, it’s an active and ongoing priority that’s felt throughout the organization.” \-Charlotte G., Sr. Underwriter \- Consultant Stop Loss*What we offer you**
Benefits and Perks
We don’t take a “one\-size\-fits\-all” approach when it comes to our employees. Our programs are designed to make your life better both at work and at home.
- Flexible full\-time or hybrid telecommuting arrangements
- Plan for your future with our 401(k) plan and take advantage of immediate vesting and company matching up to 6%
- Paid time away including vacation and sick time, flex days and ten paid holidays
- Give back to your community and double your impact through our company matching
Want more details? Check out our Symetra Benefits Overview
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Compensation
Salary Range: $106,400 \- $177,300 plus eligibility for annual bonus program
Please review Symetra’s Remote Network Minimum Requirements:
As a remote\-first organization committed to providing a positive experience for both employees and customers, Symetra has the following standards for employees’ internet connection:
- Minimum Internet Speed: 100 Mbps download and 20 Mbps upload, in alignment with the FCC's definition of "broadband."
- Internet Type: Fiber, Cable (e.g., Comcast, Spectrum), or DSL.
- Not Permissible: Satellite (e.g., Starlink), cellular broadband (hotspot or otherwise), any other wireless technology, or wired dial\-up.
When applying to jobs at Symetra you’ll be asked to test your internet speed and confirm that your internet connection meets or exceeds Symetra’s standard as outlined above.
Identity Verification
Symetra is committed to fair and secure hiring practices. For all roles, candidates will be required (after the initial phone screen) to be on video for all interviews. Symetra will take affirmative steps at key points in the process to verify that a candidate is not seeking employment fraudulently, e.g. through use of a false identity.
Failure to comply with verification procedures may result in:
- Disqualification from the recruitment process
- Withdrawal of a job offer
Termination of employment and other criminal and/or civil remedies, if fraud is discovered
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We empower inclusion
At Symetra, we aspire to be the most inclusive insurance company in the country. We’re building a place where every employee feels valued, respected, and has opportunities to contribute.
Inclusion is about recognizing our assumptions, considering multiple perspective, and removing barriers. We accept and celebrate diverse experiences, identities, and perspectives, because lifting each other up fuels thought and builds a stronger, more innovative company. We invite you to learn more about our efforts here .
Creating a world where more people have access to financial freedom
Symetra is a national financial services company dedicated to helping people achieve their financial goals and feel confident about the future. In our daily work, we’re guided by the principles of Value, Transparency and Sustainability. This means we provide products and services people need at a competitive price, we communicate clearly and openly so people understand what they’re buying, and we design products—and operate our company—to stand the test of time. We’re committed to showing up for our communities, lifting up our employees, and standing up for diversity, equity and inclusion (DEI). Join our team and help us create a world where more people have access to financial freedom.
For more information about our careers visit https://symetra.eightfold.ai/careers
Work Authorization
Employer work visa sponsorship and support are not provided for this role. Applicants must be currently authorized to work in the United States at hire and must maintain authorization to work in the United States throughout their employment with our company.
Salary Context
This $106K-$177K range is below the median 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 Symetra, 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 ($141K) sits 35% below the category median. Disclosed range: $106K to $177K.
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.
Symetra AI Hiring
Symetra has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Bellevue, WA, US, US. Compensation range: $161K - $214K.
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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