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About This Role
What You'll Do:
#### Agentic Programs Lead
#### About the Function
Avalara is seeking a highly technical, hands\-on leader to build, govern, and scale the shared AI agent fleet that powers every team across Revenue Marketing. This role sits within Revenue Marketing Programs and is the single owner of agent quality and performance org\-wide spanning Market \& Audience Intelligence, Planning \& Optimization, and Campaign Execution.
This is an individual contributor role, not a people\-management position. You will operate at Director\-level scope and influence, but your primary value is technical: designing, building, and orchestrating agents that let our GTM function scale output, efficiency, and business impact. You will partner closely with and Segment and Product GTM teams, Events, Product, Growth, and Partner Marketing teams to embed agents into their workflows, while defining the standards, guardrails, and roadmap that keep the fleet reliable and impactful.
This role reports into our Director of Revenue Programs and is central to Avalara's agentic\-first operating model where humans strategize and orchestrate, and agents execute at scale.
What Your Responsibilities Will Be:
#### Agent Strategy \& Roadmap
- Own the roadmap and prioritization for the shared agent fleet across our core pillars of: Market \& Audience Intelligence, Planning \& Optimization, and Campaign Execution
- Prioritize agent investment based on impact and scale rather than first\-in, first\-out; resource the highest\-return use cases first
- Partner with Revenue Programs and Revenue Marketing leadership to align the agent roadmap to enterprise pipeline and GTM priorities
#### Agent Development \& Deployment
- Build, test, and deploy agents across the fleet including Competitive Monitoring, Review Monitoring, Audience Sizing, Product Usage Insights, Path\-to\-Plan Planning, Channel Optimization, Campaign Performance, Brief Development \& Deployment, Messaging Testing \& Dev, Content \& Creative, Campaign Ops \& Activation, and Sequence Builder
- Work with our IT teams to own the technical architecture for agent orchestration, including integrations with core marketing systems (Marketo, Salesforce, 6sense, Power BI, ON24\)
- Establish QA, monitoring, and guardrails to ensure agents operate reliably and safely at production scale
#### Governance \& Quality
- Serve as the single owner of agent quality and performance standards across Revenue Marketing
- Define and enforce standards for agent accuracy, reliability, and responsible operation
- Manage the full agent lifecycle — build, iterate, retire — based on performance data and evolving business need
#### Cross\-Functional Enablement
- Partner with Enterprise, SMB, Product\-Led GTM, Events, Product, Growth, and Partner Marketing teams to embed the right agents into each pod's workflow
- Enable and train teams on how to use, prompt, and interpret agent outputs; act as the technical point of contact for agent\-related questions
- Translate team\-specific needs into shared, reusable agent capabilities rather than one\-off builds
#### Performance Monitoring \& Optimization
- Track adoption, utilization, and ROI of the agent fleet across every Revenue Marketing team
- Continuously optimize existing agents based on usage data, performance signals, and stakeholder feedback
- Monitor market and competitive signals to proactively evolve the fleet ahead of demand, rather than reacting to results
#### Key Metrics \& KPIs
- Agent adoption and utilization across Revenue Marketing teams and pillars
- Agent reliability and accuracy against defined quality standards
- Efficiency gains (speed\-to\-market, capacity created) enabled by the agent fleet without added headcount
- Contribution to campaign velocity, pipeline performance, and funnel conversion across teams using the fleet
- Stakeholder satisfaction and adoption rate across pods (Enterprise, SMB, Product\-Led GTM, Events, Partner Marketing)
What You'll Need to be Successful:
- 8\+ years of experience in B2B SaaS marketing technology, marketing operations, or AI/automation engineering, with a track record of designing and scaling automated or AI\-driven systems
- Hands\-on experience building and deploying AI agents or LLM\-powered workflows, including prompt engineering, orchestration frameworks, and API integrations
- Strong fluency with core B2B marketing platforms (Marketo, Salesforce, 6sense, Power BI, ON24\) and how agents plug into them
- Proven ability to operate as a senior individual contributor and technical subject\-matter expert — influencing and enabling teams without formal authority over headcount
- Solid grounding in full\-funnel B2B revenue marketing (demand generation, campaign lifecycle, funnel metrics) to ensure agents solve real GTM problems
- Experience owning technical roadmaps and governance frameworks in a matrixed organization
- Strong stakeholder management skills; able to translate technical capability into business impact for non\-technical audiences
- Comfortable operating managing agents vs humans; show a bias toward building and orchestrating rather than managing human headcount
Avalara is an AI\-first Company:
AI is embedded in our workflows, decision\-making, and products. Success here requires embracing AI as an essential capability.* You’ll bring experience using AI and AI\-related technologies, ready to thrive here.
- You’ll apply AI every day to business challenges \- improving efficiency, contributing solutions, and driving results for your team, our company, and our customers.
- You’ll grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too.
How We'll Take Care of You:
Total Rewards
In addition to a great compensation package, paid time off, and paid parental leave, many Avalara employees are eligible for bonuses.
Health \& Wellness
Benefits vary by location but generally include private medical, life, and disability insurance.
Inclusive culture and diversity
Avalara strongly supports diversity, equity, and inclusion, and is committed to integrating them into our business practices and our organizational culture. We also have a total of 8 employee\-run resource groups, each with senior leadership and exec sponsorship.
What You Need To Know About Avalara:
We’re defining the relationship between tax and tech.
We’ve already built an industry\-leading cloud compliance platform, processing over 54 billion customer API calls and over 6\.6 million tax returns a year. Our growth is real \- we're a billion dollar business \- and we’re not slowing down until we’ve achieved our mission \- to be part of every transaction in the world.
We’re bright, innovative, and disruptive, like the orange we love to wear. It captures our quirky spirit and optimistic mindset. It shows off the culture we’ve designed, that empowers our people to win. We’ve been different from day one. Join us, and your career will be too. We’re An Equal Opportunity Employer
Supporting diversity and inclusion is a cornerstone of our company — we don’t want people to fit into our culture, but to enrich it. All qualified candidates will receive consideration for employment without regard to race, color, creed, religion, age, gender, national orientation, disability, sexual orientation, US Veteran status, or any other factor protected by law. If you require any reasonable adjustments during the recruitment process, please let us know.
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 Avalara, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Avalara AI Hiring
Avalara has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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
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