Interested in this AI/ML Engineer role at Seismic?
Apply Now →Skills & Technologies
About This Role
Overview:
We are seeking a Sr. CS Transformation & AI Manger to help accelerate the next generation of Customer Success.
This role sits at the intersection of CS transformation, technology, data, and AI, with a mandate to turn our transformation strategy into new ways of working. This person will identify where AI, automation, customer intelligence, and emerging technologies can simplify work, improve decision-making, increase team capacity, and enable more proactive and impactful customer engagement.
This is not a traditional CS strategy role or a pure AI engineering role. We are looking for a transformation leader with deep applied-AI fluency and a builder's mindset—someone who can connect business problems to technical possibilities, rapidly prototype solutions, and partner across CS, Product, Engineering, Data, and Operations to bring the best ideas to scale.
The opportunity is bigger than deploying AI tools. It's about fundamentally rethinking how Customer Success operates in an AI-enabled world.
Who you are::
You are a strategic problem solver, technologist, builder, and transformation leader, who sees AI not simply as another tool, but as an opportunity to fundamentally rethink how Customer Success operates.
You bring:
- Deep understanding of Customer Success, SaaS, and GTM operating models.
- Strong applied-AI fluency and a point of view on how AI will reshape Customer Success.
- A builder's mentality with an operator's discipline—comfortable experimenting and prototyping, while accountable for getting solutions adopted and scaled.
- Strong systems thinking across people, process, data, technology, and customer experience.
- The ability to move comfortably between business strategy and technical execution.
- A bias toward action and a track record of turning ambiguous problems into tangible solutions.
- Strong cross-functional influence and the ability to create momentum across complex organizations.
- Curiosity and a desire to continuously experiment, learn, and push what's possible.
Qualifications & Experience
Required
- 6+ years of experience across Customer Success, SaaS, GTM transformation, technology strategy, product, AI/automation, or related disciplines.
- Demonstrated experience leading cross-functional transformation or technology initiatives within CS, post-sales, or broader GTM organizations.
- Proven experience applying AI, automation, and data to solve real business problems.
- Strong working knowledge of modern AI capabilities, including LLMs, AI agents, APIs, integrations, workflow automation, and enterprise data.
- Hands-on experience prototyping AI-enabled workflows, agents, automations, or similar solutions.
- Experience with enterprise GTM technology ecosystems such as Salesforce, Gainsight or comparable CS platforms, data/BI platforms, and workflow automation technologies.
- Demonstrated ability to partner effectively with Engineering, Data, Product, IT, Security, and Operations.
- Proven ability to move complex initiatives from strategy through execution, adoption, and measurable business impact.
What Success Looks Like
Success means we aren't simply using more AI, we are operating Customer Success differently because of it.
CS teams spend less time searching for information and performing manual work, customer intelligence becomes more proactive and actionable, and teams have greater capacity to focus on strategic customer engagement and value.
Most importantly, AI and transformation become a measurable business capability—not a collection of tools, pilots, and experiments
What you'll be doing:: Transform the CS Operating Model
- Partner with CS leadership to translate the future-state vision for Customer Success into clear initiatives, workstreams, and execution plans.
- Evaluate how work gets done today and identify where workflows should be eliminated, automated, augmented, or fundamentally redesigned.
- Lead high-priority transformation initiatives that improve CS productivity, scalability, proactive engagement, and customer impact.
- Bring a builder's mentality with an operator's discipline - moving beyond ideas and pilots to capabilities that are adopted, scaled, and delivering measurable value.
- Coordinate across teams, manage dependencies, surface risks, and keep transformation initiatives moving with speed.
Advance the CS Intelligence Layer
- Help evolve the intelligence layer that connects customer signals across systems and uses AI to identify what matters, why it matters, and what action should happen next.
- Partner across CS, Data, Product, and Operations to bring together signals such as product adoption, engagement, sentiment, support, relationship, lifecycle, and commercial data.
- Translate customer intelligence into prioritized insights, risk and opportunity signals, and next-best actions for CS teams.
- Help embed intelligence directly into CS workflows rather than creating more dashboards, tools, or places for teams to look.
- Create closed-loop mechanisms that turn customer and CSM signals into intelligence, action, and measurable outcomes.
Architect & Rapidly Prototype AI-Enabled Solutions
- Translate CS business problems into AI-enabled workflows, solution concepts, data requirements, and technical approaches.
- Rapidly prototype agents, automations, and AI-enabled workflows to test ideas and demonstrate what's possible, and accelerate decision-making.
- Bring enough technical depth to work credibly across LLMs, agents, APIs, integrations, automation, enterprise data, and modern AI platforms.
- Evaluate potential solutions and provide recommendations on when to build, buy, integrate, or leverage existing enterprise capabilities.
- Partner closely with Engineering, Data, IT, Security, and Architecture teams to transition validated concepts into secure, scalable, production-ready capabilities.
This role is not expected to be the production engineer for every solution. It does require someone technical enough to explore, prototype, make informed solution decisions, and effectively bridge the gap between Customer Success strategy and technical execution.
Drive Adoption & Business Impact
Move initiatives from idea prototype pilot production adoption* measurable impact.
- Partner with CS leaders and Enablement to ensure new capabilities translate into meaningful workflow and behavior change.
- Establish success criteria and feedback loops to continuously improve solutions based on field usage and customer impact.
- Measure transformation through improvements in productivity, capacity, adoption, proactive risk identification, retention, growth, and customer outcomes.
- Provide clear visibility into progress, impact, dependencies, and decisions required to keep transformation moving with speed.
What we have for you::
At Seismic, we’re committed to providing benefits and perks for the whole self. To explore our benefits available in each country, please visit the Global Benefits page.
If you are an individual with a disability and would like to request a reasonable accommodation as part of the application or recruiting process, please click here.
Seismic is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to gender, age, race, religion, or any other classification which is protected by applicable law. :
We are committed to fair and equitable compensation practices.
Seismic's annual base salary range for this position will vary based on applicant's location, experience, job level, skills, and abilities as well as internal equity and alignment market data. The range listed below is the minimum to the maximum of our target hiring range.
Seismic's salary range for this position is: USD $123,100.00/Yr. - USD $212,300.00/Yr.
This position is also eligible to participate in Seismic's incentive plans in addition to base salary. The actual incentive amount will very and will be subject to the terms and conditions set in the applicable incentive plan.
Salary Context
This $123K-$212K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Seismic, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $123K to $212K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Seismic AI Hiring
Seismic has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in US. Compensation range: $180K - $212K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.