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About This Role
Overview:
Working at Atlassian
Atlassian can hire people in any country where we have a legal entity. Assuming you have eligible working rights and a time zone overlap with your team, you can choose to work remotely or in an office. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.
Your future team
The Atlassian Advisory Services team is a globally distributed team of Atlassian experts who are passionate about creating customer success. Advisory Services team members engage with our largest strategic and enterprise organizations with some of the most complex business challenges to help them deliver delightful solutions to their users. We provide customer teams with trusted advisors to help orchestrate successful outcomes, providing the highest benefits from their Atlassian investment. At Atlassian, you'll have an impact on millions of users, fast! We don't just want to know your opinions, we want to see your ideas in action. We hire great people and then trust them to be great.
We're hiring a Senior Solution Consultant with a Cloud Platform and AI focus to join our Advisory Services Delivery team as an individual contributor - this is not a managerial role. AtlassianSenior Solution Consultantsare experts in Atlassian products, practices, and solutions, delivering guidance to drive value realization for clients that have purchased Advisory Services from Atlassian. Each member of the team is accomplished at delivering performant strategic technical guidance at scale, aligning product capabilities with business needs and desired outcomes.
You will help our strongest promoters showcase successes to their peers, and serve as the tip of the spear in growing the reach of our technologies for new use cases and markets. Our aim is to help customers unleash the potential of their teams and get the most out of their Atlassian investment.
Responsibilities:
What you'll do:
- Collaborate with your peers in Advisory Services to align on strategic outcomes that deliver exceptional service to our customers
- Partner with customers to help solve their business challenges and achieve their internal goals through Atlassian products, practices, and solutions
- Identify and promote opportunities for service and product expansion within a client’s organization
- Cultivate deep industry and solution expertise, staying up-to-date with evolving best practices that support different types of teams
- Create technical solution content and prescriptive guidance to support customer goals and outcomes in one or more Atlassian solution areas
- Partner with non-customer-facing teams across Atlassian to advocate for customer needs and innovative solutions that address their business drivers
- Spend up to 30% of your time traveling domestically, and in some cases internationally, for both internal and customer-facing events
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: $159,300 - $207,975
Zone B: $143,370 - $187,178
Zone C: $132,219 - $172,619
Qualifications:
Your background:
- 6-8 years of experience within SaaS companies
- 5+ years of experience in customer-facing roles where you’ve engaged with and influenced customer stakeholders that range from technical administrators to executive leadership
- Demonstrated expertise in our Cloud Platform, including:
+ Experience administering Atlassian Cloud ecosystems including applications like Guard, Jira Software, Jira Service Management, Confluence, Focus, Rovo, etc + Background in SaaS based-architectures, integration methods, and associated security considerations + Experience performing Atlassian’s Cloud migration process at varying customer scales, and expertise in associated migration utilities, and hybrid SaaS and on-premise deployment models
- Demonstrated expertise in Artificial Intelligence, including
+ Familiarity with Atlassian’s Rovo platform, and ways in which Agents are implemented and deployed + Background in large language models (LLMs) for generative content, prompt engineering, and chat-mediated interactions + Proven ability to design proof-of-concept solutions to address common business challenges for Enterprise customers
- You're fluent in English, and nice to have a second language such as Spanish, French, or Portuguese for business communication.
It’s nice if you are:
- Comfortable coaching best practices and tailoring curated guidance for stakeholders to facilitate those practices
- Experienced working on cross-team projects, engaging with Sales, Product, Support, and other teams
- Experienced working with large customers in a consulting or technical expert capacity
Our perks & benefits
To support you at work and play, our perks and benefits include ample time off, an annual education budget, paid volunteer days, and so much more.
About Atlassian
The world's best teams work better together with Atlassian. From medicine and space travel, to disaster response and pizza deliveries, Atlassian software products help teams all over the planet. At Atlassian, we're motivated by a common goal: to unleash the potential of every team.
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 learn more about our culture and hiring process, explore our Candidate Resource Hub.
Salary Context
This $132K-$207K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $132K to $207K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Atlassian AI Hiring
Atlassian has 19 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span San Francisco, CA, US, Mountain View, CA, US, Seattle, WA, US. Compensation range: $162K - $309K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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