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About This Role
Overview:
Working at Atlassian
Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.
### What you'll do
In this role, you'll be part of the Customer Success Architect team within the Sales and Success department, focusing on the adoption of Atlassian's AI platform, Rovo. As a CSA (AI), you'll execute time\-boxed, co\-built technical engagements that take customers from "Rovo is turned on" to real, measurable business value in production. You'll partner directly with enterprise teams to discover high\-impact AI use cases, design and build custom Rovo agents, configure third\-party connectors, and deploy working solutions into production workflows. Your role bridges deep AI and Atlassian platform expertise with customer success, focusing on measurable adoption outcomes. Your work will involve managing engagements, enabling customers, and continuously improving processes to enhance customer satisfaction and operational efficiency.
We're hiring a CSA (with an AI focus) to join our Customer Success Architect team, reporting to a regional leader. This is not a managerial role. Atlassian CSAs are technical experts with Atlassian solutions, delivering guidance to drive value realization and adoption. CSAs are accomplished at delivering performant technical guidance at scale, aligning product capabilities with business needs and desired outcomes. They partner with Pre\-Sales, Post\-Sales, and Product teams to provide solutions and technical guidance to help customers achieve their desired outcomes and goals.
You will help our strongest promoters showcase their successes to their peers and serve as the tip of the spear in expanding the reach of AI and the Atlassian System of Work into new use cases and markets. Atlassian CSAs aim to help customers get the most out of their Atlassian investment.
Responsibilities:
### On your first day, we'll expect you to have:
- Discovery. Facilitate use case discovery workshops with customer teams to identify high\-impact, high\-value AI opportunities. Understand their workflows, pain points, and where Rovo agents and search can deliver measurable outcomes.
- Design. Co\-build custom Rovo agents and configure third\-party connectors alongside customer teams. Customers are hands\-on keyboard. Design solutions tailored to their workflows using the Atlassian platform (Jira, Confluence, Rovo Studio).
- Deploy. Guide customers from sandbox to production. Validate that agents are performing, connectors are flowing data, and teams are enabled to iterate and scale independently post\-engagement.
- Collaborate. Work with Account Teams and Customer Success Managers (CSMs) to plan customer\-specific activation journeys and hand off for sustained growth.
- Advocate. Meet with Rovo Product, Engineering, and AI teams to surface product friction, share field patterns, and improve the adoption motion.
- Travel up to 15% domestically and internationally for events and customer meetings.
### Your background
- Customer Success \& Experience: 3\-5 years in Customer Success or account management, managing Enterprise customers with complex SaaS portfolios.
- Technical Aptitude: Experience with AI platforms, the Atlassian ecosystem (Jira, Confluence, Rovo), and technical product demos. Familiarity with building AI agents, prompt engineering, and configuring integrations between enterprise knowledge sources and AI systems. Hands\-on experience helping organizations adopt and operationalize AI tools in production environments.
- Solution Area Expertise: Specialized knowledge in enterprise workflows across product development, IT service management, or business operations. Understanding how AI agents and automation can improve processes such as incident response, backlog management, knowledge discovery, and cross\-team collaboration. Familiarity with third\-party connector ecosystems (Google Drive, SharePoint, Slack, GitHub, etc.).
- Product Mindset \& Communication: Strong product mindset, able to shape technical solutions for business impact, and comfortable owning problems end\-to\-end from discovery to deployment. Effective communicator who can influence stakeholders and explain AI concepts to varied audiences. Delivers durable solutions but can quickly prototype when needed. Ability to translate AI capabilities into tangible outcomes for non\-technical stakeholders.
Compensation:
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.
This role may also be eligible for benefits, bonuses, commissions, and equity.
Pay Ranges:
In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:
Zone A: $147,600 \- $192,700
Zone B: $133,200 \- $173,900
Zone C: $122,508 \- $159,941
Qualifications:
### Your background
- Customer Success \& Experience: 3\-5 years in Customer Success or account management, managing Enterprise customers with complex SaaS portfolios.
- Technical Aptitude: Experience with AI platforms, the Atlassian ecosystem (Jira, Confluence, Rovo), and technical product demos. Familiarity with building AI agents, prompt engineering, and configuring integrations between enterprise knowledge sources and AI systems. Hands\-on experience helping organizations adopt and operationalize AI tools in production environments.
- Solution Area Expertise: Specialized knowledge in enterprise workflows across product development, IT service management, or business operations. Understanding how AI agents and automation can improve processes such as incident response, backlog management, knowledge discovery, and cross\-team collaboration. Familiarity with third\-party connector ecosystems (Google Drive, SharePoint, Slack, GitHub, etc.).
- Product Mindset \& Communication: Strong product mindset, able to shape technical solutions for business impact, and comfortable owning problems end\-to\-end from discovery to deployment. Effective communicator who can influence stakeholders and explain AI concepts to varied audiences. Delivers durable solutions but can quickly prototype when needed. Ability to translate AI capabilities into tangible outcomes for non\-technical stakeholders.
Benefits \& Perks
Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits.
About Atlassian
At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.
We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.
To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.
To learn more about our culture and hiring process, visit go.atlassian.com/crh.
In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.
Salary Context
This $122K-$192K range is below the median 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 Atlassian, 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 ($157K) sits 27% below the category median. Disclosed range: $122K to $192K.
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.
Atlassian AI Hiring
Atlassian has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span San Francisco, CA, US, Austin, TX, US, Mountain View, CA, US. Compensation range: $192K - $309K.
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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