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About This Role
About Workato
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Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI\-powered platform enables teams to navigate complex workflows in real\-time, driving efficiency and agility.
Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today's fast\-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com.
Why join us?
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Ultimately, Workato believes in fostering a flexible, trust\-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.
But, we also believe in balancing productivity with self\-care. That's why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.
If this sounds right up your alley, please submit an application. We look forward to getting to know you!
Also, feel free to check out why:
- Business Insider named us an "enterprise startup to bet your career on"
- Forbes' Cloud 100 recognized us as one of the top 100 private cloud companies in the world
- Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
- Quartz ranked us the \#1 best company for remote workers
Responsibilities
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We are looking for an exceptional Staff Domain Expert, Customer Support to join our growing AI Business Solutions team. In this role, you will serve as the domain expertise and product management engine for Workato's Customer Support AI agents and apps — translating deep knowledge of CX, service operations, and support workflows into production\-grade AI agents and apps that drive measurable customer outcomes. You will also be responsible for:
- Leading the discovery and capture of Customer Support\-domain AI agent and app concepts through customer engagement, field collaboration, and internal research — building a library of high\-value AI use cases across support, service delivery, and CX functions
- Authoring PRDs for Customer Support AI agents and apps, shepherding concepts from initial capture through design partner validation to production deployment
- Running targeted AI workshops with top Customer Support accounts — facilitating hands\-on sessions that accelerate AI agent adoption and produce referenceable, production\-grade customer outcomes
- Partnering with Workato's internal teams to bring learnings from customer engagements back to the business — ensuring Workato itself is the leading example of what AI transformation looks like in practice
- Collaborating with Sales and Customer Success to position and land Customer Support AI use cases in the enterprise, supporting pipeline generation and expansion
- Developing Customer Support\-specific AI thought leadership, POVs, and customer stories that demonstrate Workato's impact across service and CX organizations
- Working with Field Readiness to build messaging and enablement for selling Customer Support AI agents to CX, ITSM, and service operations personas
Requirements
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### Qualifications / Experience / Technical Skills
- 7\+ years in a customer\-facing or domain specialist role with a Customer Support or CX focus (e.g., support operations, service delivery, ITSM, or solutions consulting for CX/support technology)
- Deep understanding of Customer Support technology, workflows, and personas (VP CX, Head of Support, ITSM leads, CS Ops) across multiple industries
- Experience scoping and delivering automation or AI solutions in support environments; familiarity with tools such as Salesforce Service Cloud, Zendesk, ServiceNow, Intercom, Freshdesk, or similar
- Experience with AI tools, large language models, or agentic workflow platforms; hands\-on experience with Workato (AI agents, automation recipes, integration platforms) is a strong plus
- Ability to translate business problems into structured product requirements (PRD authorship or equivalent)
- Proven track record influencing revenue — through quota\-carrying roles, pipeline generation, or expansion in a customer\-facing capacity
- Located in North America with flexibility to travel
### Soft Skills / Personal Characteristics
- Ownership mindset — operates end\-to\-end from concept capture to production deployment with no handoff gaps
- Velocity over consensus — iterates fast, finds the gaps, and acts on the growth edge rather than waiting for everyone to align
- Customer\-first — every use case starts and ends with a real customer problem; outcomes over optics
- Comfortable building in ambiguity — creates new playbooks rather than fitting into established ones
- Communicates credibly with senior buyers — can engage VPs of CX, Heads of Support, and service leaders as a peer and hold the room
- 10X thinker — identifies bets that 10x customer value, not incremental improvements
The estimated pay for this role tanges between $150,000 – $180,000 plus variable, benefits, perks and equity.
Job Req ID: 2669
Salary Context
This $150K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Workato, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Workato AI Hiring
Workato has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Palo Alto, CA, US. Compensation range: $180K - $285K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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