Interested in this AI/ML Engineer role at Workiva?
Apply Now →Skills & Technologies
About This Role
Join our team at Workiva as an Manager of Machine Learning \- AI Modeling and Operation ! As a pivotal member of our AI/ML team, you’ll own the infrastructure that makes every AI feature at Workiva reliable, observable, and deployable.
You will lead the team responsible for ML infrastructure, model operations, and AI quality at Workiva. Your team owns the systems that make AI reliable in production from model lifecycle management to evaluation frameworks, observability, and model routing across frontier providers. You'll build the operational backbone that every AI feature at Workiva depends on.
Join us if you want to own the infrastructure layer that makes enterprise AI work at scale, not just build demos. Discover more about Workiva's Generative AI .
What You’ll Do
Operational Excellence
- Own the ML model lifecycle: training pipelines, model registry, deployment, monitoring, and guardrails
- Build and maintain CI/CD for ML \- automated testing, evaluation, and promotion of models across environments
- Drive observability across AI services: latency tracking, drift detection, cost monitoring, alerting
- Establish SLOs/SLIs for AI services and lead incident response for ML\-related production issues and maintain high service availability
- Reduce complexity through simplification, automation, and thoughtful system design
### Leadership \& Team Management
- Lead and grow an existing strong team of machine learning engineers
- Provide hands\-on coaching, performance feedback, and growth opportunities for engineers at varying experience levels
- Foster a collaborative, inclusive, and high\-ownership team culture grounded in trust, accountability, and continuous improvement
### Cross Functional Collaboration
- Partner with Intelligence pillar engineering squads (AGFW, AIEI, AIQG, Applied AI, Search) and Product teams to ensure AI services are production\-ready, operationally sound, and observable
- Communicate complex technical issues to both technical and non\-technical audiences effectively
Technical Strategy \& Execution
- Manage integrations with frontier model providers (AWS Bedrock, Azure OpenAI, Google) including model routing, load balancing, and fallback strategies
- Drive AI analytics dashboards that give leadership and product visibility into platform health and usage
- Drive improvements in latency, service availability, developer experience, and integration usability across internal and external interfaces
- Guide architectural decisions to ensure platform scalability, reliability, and alignment with Workiva’s long\-term technical vision
What You’ll Need
Minimum Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science or equivalent combination of education and experience
- 7\+ years of total experience in software engineering and/or Machine Learning, with at least 2 years of dedicated experience as an Engineering Manager
- Experience with ML pipeline orchestration tools (ClearML, Kubeflow, Airflow, or similar)
- Hands\-on background with Kubernetes, microservices, container orchestration, and infrastructure\-as\-code
- Track record of improving reliability/availability metrics for production ML systems
- Proven ability to manage senior individual contributors, resolve technical conflicts, and build a culture of psychological safety and high performance
- Solid leadership skills in an Agile/Sprint working environment
- Experience operating production ML systems in cloud environments (AWS, Azure, or GCP)
### Preferred Qualifications
- Master’s degree in Computer Science, Engineering, Data Science or equivalent combination of education and experience
- Experience with core concepts of Generative AI such as RAG, Agentic frameworks, etc
- Experience building model evaluation or quality measurement systems
- Familiarity with cost optimization for GPU/model serving workloads
- Familiarity with observability tooling (Datadog, Prometheus, Grafana)
### Working Conditions
- Willingness to travel up to 15% for team and corporate meetings, fostering relationships and representing company interests
- Reliable internet access for remote working opportunities
How You’ll Be Rewarded
✅ Salary range in the US: $163,000\.00 \- $290,000\.00
✅ A discretionary bonus typically paid annually
✅ Restricted Stock Units granted at time of hire
✅ 401(k) match and comprehensive employee benefits package
The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.
Why Join Workiva
Workiva is the platform designed to bring confidence, control, and a competitive edge to the world’s most complex organizations. Our AI\-powered platform unifies finance, risk, and sustainability on a single, secure foundation—ensuring data is trusted, traceable, and ready to act on. With an unbroken path from source to output, leaders gain confidence in their numbers, visibility into current and emerging risks, and the ability to move with speed and precision in a constantly changing world.
At Workiva, you’ll bring technology to market that executives, boards, and regulators depend on. The work you do here helps organizations navigate uncertainty, maintain trust, and make decisions that stand up to scrutiny. If you’re energized by meaningful challenges, inspired by collaborative teams, and motivated to help organizations turn uncertainty into advantage, we’d love to meet you.
Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic.
Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email [email protected] .
Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards.
*Workiva supports employees in working where they work best \- either from an office or remotely from any location within their country of employment.*
\#LI\-MJ2
Salary Context
This $163K-$290K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Workiva, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($226K) sits 5% above the category median. Disclosed range: $163K to $290K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Workiva AI Hiring
Workiva has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $194K - $308K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.