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About This Role
This is a hybrid position requiring in\-office attendance one day per week at our Atlanta office. Candidates must reside within a commutable distance to be considered.
Fullstory Anywhere is one of Fullstory's three primary product verticals, and it's growing fast. We put Fullstory's rich digital experience data directly into customers' hands: in their warehouses, in their AI workflows, and in the tools their teams already use. As our Senior Data Engineer, you will report to the Senior Engineering Manager for the Fullstory Anywhere team and help build the systems that transform, move, and activate billions of data points at massive scale so that customers can unlock insights in their own environments and build intelligent agents on top of real user behavior.
You will design and optimize pipelines that process 30 billion\+ records per day across customer warehouses, collaborate with product and ML engineers to define how LLM\-powered customer agents evaluate and act on Fullstory data, and make architectural decisions that balance throughput, cost, and reliability across a product vertical with accelerating revenue and adoption.
To excel in this job, you must be comfortable owning large, ambiguous technical problems end\-to\-end, from initial design through production health, and know how to build data\-intensive systems that stay reliable as they scale.
In a typical day, you might:
- Maintain, extend, and scale Go microservices that transform and deliver Fullstory session data into customer warehouses and power the team's MCP server that enables AI agent integrations.
- Develop and maintain dbt models and pipeline orchestration to ensure timely, fault\-tolerant data migrations across hundreds of customer destinations.
- Define evaluation frameworks for LLM outputs using tools like Langsmith and Vertex AI, ensuring AI\-powered customer agents produce accurate, useful results.
- Investigate and resolve production incidents across the data pipeline, implementing systemic fixes that prevent entire classes of failure from recurring.
- Write technical design documents that drive consensus on architectural changes, proactively surfacing scaling bottlenecks, edge cases, and cross\-team dependencies.
- Demonstrate sound technical judgment by de\-risking work through spikes, taking on tech debt deliberately, and knowing when to escalate versus dig in.
Here’s what we’re looking for:
- Significant experience building and operating high\-throughput data pipelines (batch and/or streaming) in a major cloud platform, including work with cloud data warehouses like BigQuery, Snowflake, or Databricks.
- Proficiency in Go, Python, Java or a similar language.
- Hands\-on experience with data transformation tooling such as dbt, with a strong understanding of data modeling and pipeline observability.
- Familiarity with LLM integration patterns and evaluation approaches (e.g., LangSmith, Vertex AI, or comparable frameworks), or demonstrated ability to ramp quickly in applied AI.
- A track record of owning major system areas end\-to\-end: driving architectural decisions, maintaining production health, and improving reliability over time.
The impact you will have in 6 Months:
- You will own a critical segment of the warehouse transformation pipeline, having shipped improvements that measurably increase throughput, reduce latency, or improve data quality for customers.
- You will have helped establish initial evaluation criteria and tooling for LLM\-generated outputs in the customer agent platform, giving the team a repeatable way to measure and improve AI quality.
The impact you will have in 12 Months:
- You are a technical authority on the data pipeline architecture, driving end\-to\-end improvements and mentoring peers on system design and operational excellence.
- You have helped shape the AI agent platform's technical direction, from eval frameworks to MCP server tooling, and are leading initiatives that expand what customers can build with Fullstory data.
The base salary for this position ranges between $160,000 \- $170,000 USD. Base salary will vary based on relevant experience, job\-related skills and qualifications. This role is also eligible for a discretionary bonus of up to 10% of base salary, contingent upon Fullstory meeting its performance targets.
\#LI\-Hybrid \#LI\-BS1
### About Fullstory
Fullstory is a leading behavioral data platform transforming how businesses understand and improve their digital experiences. Our technology empowers companies to uncover insights, optimize customer and employee journeys, and deliver exceptional online interactions. It makes every customer experience smoother and more personalized and empowers the employees behind the scenes.
We’re building something special at Fullstory\- and we’re looking for teammates who are curious, collaborative, and driven to make an impact. We’re especially excited about the potential of AI to enhance efficiency, spark creativity, and elevate our work. If that resonates, explore our Winning Ways to see the values that guide how we work and grow together.
How we support you:
Fullstorians are committed to building something better\- from how we approach our product, to how we care for our customers and each other. Along these lines, we offer:
- Flexibility and Connection. We have a vibrant HQ in Atlanta and a tight\-knit group in London. Fullstorians in those cities come to the office at least one day a week to build cross\-functional relationships and stay connected. We also offer a flexible PTO policy and an annual company\-wide closure, along with federal holidays.
- Benefits. Take care of the whole you. Fullstory offers sponsored benefit packages for US\-based Fullstorians, and supplemental coverage options for international Fullstorians.
- Learning opportunities. We provide professional development opportunities through training programs and an annual learning subsidy for US and EMEA\-based employees.
- Productivity support. US and EMEA\-based Fullstorians receive a monthly productivity stipend.
- Team Collaboration. Connect with fellow Fullstorians through team off\-sites and an annual full\-company meet\-up.
- Paid parental leave. Fullstorians balance the needs of their growing families without the added stress of figuring out work and finances.
- Bereavement leave, including miscarriage/pregnancy loss. Take the time to grieve and help your loved ones.
*Fullstory is proud to be an equal\-opportunity workplace dedicated to fostering an increasingly diverse community. We want candidates of all human varieties, backgrounds, and lifestyles. There’s no problem that can’t be made better by bringing together people with a broader set of perspectives.* *If our product, values, and community resonate with you, please apply– we'd love to hear from you!*
Salary Context
This $160K-$170K 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 fullstory, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 9% below the category median. Disclosed range: $160K to $170K.
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.
fullstory AI Hiring
fullstory has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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
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