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About This Role
Build your future with Sovos.
If you're seeking a career where innovation meets impact, you've come to the right place. As a global leader, Sovos is transforming tax compliance from a business requirement to a force for growth while revolutionizing how businesses navigate the ever\-changing regulatory landscape.
At Sovos, we're dedicated to more than just solving compliance challenges \- we're committed to making a positive and lasting difference in everything we do. Our teams operate on the modern edge of digital technology, working not only to solve complex business challenges but also to enrich our personal, professional, and local communities.
Our purpose\-built systems provide the tools you need to thrive in a world where governments demand increased visibility, faster reporting and greater control over business processes. Excited about the possibilities? So are we!
Don't worry if you don't check all the boxes \- apply anyway! We're focused on hiring the right people, not just the "right" resume. It's not about what you've done elsewhere; it's all about what you're capable of doing here.
The Work You'll Do:
At Sovos, we're transforming how companies around the world manage tax compliance, helping half of the Fortune 500 and thousands more stay ahead in a complex, ever\-changing global regulatory landscape. As a GTM AI Analyst, you'll serve as the hands\-on owner and primary coordinator for the technology portfolio supporting Sovos' go\-to\-market (GTM) organization. You'll drive seller and team productivity by administering and optimizing GTM tools with a focus on artificial intelligence, identifying opportunities to simplify work, increasing adoption, and applying automation to reduce manual effort and improve execution.
Working across Sales, Marketing, Revenue Operations, IT, Security, Finance, and other partners, you'll translate business needs into practical tool and workflow solutions. This role blends business analysis, systems fluency, structured problem\-solving, change management, and a strong orientation toward measurable business value.
*More specifically, you will:*
- Define the GTM technology strategy and roadmap based on business priorities, productivity opportunities, and evolving organizational needs; assess the current portfolio for capability gaps, duplication, and underutilized functionality
- Translate GTM productivity challenges into clear business and functional requirements; evaluate available tools, modules, AI capabilities, and automation options; develop business cases; and recommend solutions that deliver the greatest operational value and return on investment
- Partner with Sales Operations, IT, system administrators, Security, and vendors to translate approved technology decisions into implementation requirements and validate that delivered solutions meet intended business outcomes
- Identify repeatable GTM activities that can be improved through generative AI, workflow automation, intelligent assistants, and other emerging capabilities; prototype solutions, document requirements, coordinate testing, and support responsible rollout
- Analyze seller and team workflows to identify unnecessary steps, duplicate work, disconnected tools, adoption barriers, and opportunities to simplify or automate processes
- Gather and document business requirements, user stories, process flows, dependencies, and expected outcomes; coordinate with technical teams and vendors through testing, deployment, and adoption
- Develop practical user guidance, training materials, office hours, launch communications, and adoption plans; gather feedback and reinforce standard ways of working
- Partner with IT, Security, Legal, Privacy, and Data teams to ensure tools and AI use cases follow approved standards for access, data handling, compliance, model use, and responsible deployment
- Define and track adoption, utilization, time savings, user satisfaction, process performance, and business impact; provide regular reporting and recommendations to GTM leadership
- Build strong working relationships across the GTM organization, balancing user needs, strategic priorities, system constraints, budget considerations, and enterprise standards
What We Need From You
- Three or more years of experience in Revenue Operations, Sales Operations, Marketing Operations, Customer Success Operations, business systems, business analysis, or a related GTM function
- Hands\-on experience supporting, administering, implementing, or optimizing Software as a Service (SaaS) business tools
- Demonstrated ability to document end\-to\-end business processes, identify gaps and inefficiencies, and translate business needs into clear requirements and technology\-enabled solutions
- Working knowledge of Salesforce and common GTM technology categories such as sales engagement, enablement, conversation intelligence, forecasting, data enrichment, customer success, analytics, or productivity platforms
- Demonstrated experience improving processes, supporting technology adoption, and coordinating work across business and technical stakeholders
- Strong analytical skills, attention to detail, documentation discipline, and the ability to manage multiple priorities
- Clear written and verbal communication skills, including the ability to explain technical concepts in business terms
- Experience with global, matrixed, or multi\-product go\-to\-market organizations (preferred)
- Understanding of data integrations, application programming interfaces (APIs), identity/access concepts, data quality, and systems governance (preferred)
- Experience supporting vendor evaluations or technology business cases (preferred)
- Familiarity with agile delivery practices, project management, or change management methods (preferred)
- Experience using generative AI, copilots, workflow automation, or low\-code/no\-code platforms in a business environment (preferred)
- Due to client contractual obligations, the successful candidate will be asked to clear a background check and drug test upon hire
What Does Sovos Offer You?
The tools to enhance your life \- because we want you to enjoy your life outside of work and inside!
- Flexible Time\-Off
- Comprehensive Health, Dental and Vision benefits
- 401(k) with employee sponsored match
- Bi\-Weekly Meeting Free Days
- Mentoring Programs
- Globally recognized Training and Development programs
- Tuition Reimbursement, Time off to Volunteer, Charitable Giving Match, and more!
Sovos is an equal opportunity employer committed to providing an environment that celebrates diversity and where equal employment opportunities are available to all applicants and employees. We do not discriminate against race, color, religions, national origin, age, sex, marital status, physical or mental disability, veteran status, gender identity, sexual orientation, or any other characteristic provided by law. At Sovos, all employees are encouraged to bring their whole selves to work.
Company Background
Sovos is a global provider of tax, compliance and trust solutions and services that enable businesses to navigate an increasingly regulated world with true confidence. Purpose\-built for always\-on compliance capabilities, our scalable IT\-driven solutions meet the demands of an evolving and complex global regulatory landscape. Sovos' cloud\-based software platform provides an unparalleled level of integration with business applications and government compliance processes.
More than 100,000 customers in 100\+ countries \- including half the Fortune 500 \- trust Sovos for their compliance needs. Sovos annually processes more than three billion transactions across 19,000 global tax jurisdictions. Bolstered by a robust partner program more than 400 strong, Sovos brings to bear an unrivaled global network for companies across industries and geographies. Founded in 1979, Sovos has operations across the Americas and Europe, and is owned by Hg and TA Associates. For more information visit http://www.sovos.com and follow us on LinkedIn and Twitter.
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 Sovos, 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.
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.
Sovos AI Hiring
Sovos has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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