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Overview: Working at Atlassian
Atlassians have the flexibility to work where they can collaborate most effectively with their team, whether at home, one of our global offices, or both.
As a distributed-first company, all interviews are conducted virtually. We hire in any country where we have a legal entity. Learn how we work at go.atlassian.com/distributed.
Your Future Org
Atlassian’s Intern program combines hands-on technical training, professional growth opportunities, dedicated mentorship, and strong social connections. This holistic approach empowers students to hit the ground running and sets them up for a successful and impactful career at Atlassian!
This position is not eligible for F-1 and J-1 students. Visa sponsorship is not available for this role.
Our paid internship program runs for 12 weeks between May/June 2027 – August/September 2027.
Your Future Team
Join Atlassian as an intern and spend your summer with us having an impact on how millions of users see and work with the Atlassian brand and products. As an intern for the Global Demand Campaigns team, you'll focus on helping teams worldwide—from startups to Fortune 500 companies—get the most out of Atlassian tools. You'll collaborate with Demand Generation Strategists, Product Marketing, and Marketing Operations to create and launch compelling marketing campaigns that bring our products to life. This opportunity as a Demand Generation intern will give your career a head start with support from a collaborative, multi-disciplinary team and experience shaping the direction of our key AI marketing programs.
The Global Demand Campaigns (GDC) team crafts and executes digital demand strategies for multiple products and collections across Atlassian. We run our organization like a well-oiled agile team, where top-priority items are easily identifiable and everyone rallies around shared deliverables and each other. An internship with Atlassian will provide you with foundational knowledge across digital demand generation, including ideal customer profiling, persona-based campaigns, campaign channels, attribution, and account-based marketing (focused on 1:many).
Responsibilities: Responsibilities
- Pilot a closed-loop AI account engagement engine that decides which accounts to pursue, executes personalized outreach, and hands sales-ready accounts to sellers, putting the "Human + AI Enterprise" story to work on our own funnel.
- Analyze Account & Prospect Intelligence that enriches each account and key contact with third-party firmographic (industry, size, growth signals), technographic (tech stack, including Jira footprint and competing SPM tools), and buying-committee data.
- Use AI (The Brain) to understand and apply a propensity + next-best-action model that ranks target accounts and recommends the right move for each (nurture, event invite, or escalate to sales).
- Build an AI agent (The Hands) that executes the recommended nurture, generates seller-ready account briefings, and notifies the account owner in Slack when an account becomes sales-ready, with a human approving every send. Outcomes feed back into the model so it improves across the three-month internship.
Key Deliverables
- Account & prospect enrichment layer (firmographic, technographic, buying-committee)
- Account propensity model + next-best-action recommendations (ranked, action-tagged target list)
- A working (human-in-the-loop) AI nurture agent running against a matched baseline cohort
- The Account Brief generator: auto-produced, Confluence-ready seller one-pagers
- Seller notification / Slack delivery when an account crosses the sales-ready threshold
- A controlled pilot with before/after measurement, an executive results readout, and a packaged, repeatable playbook
Measures of Success
- Overall outcome/impact: A measurable MQL-to-SQL conversion lift (agent-engaged cohort vs. matched baseline), plus account-progression velocity improvement and a clear decision on whether to scale the motion.
- Key deliverables completion status: Enrichment layer, propensity/next-best-action model, AI nurture agent, account-brief generator, Slack delivery, controlled pilot, and packaged playbook all completed and handed over.
- Craft Excellence: Account briefs are accurate, on-message, and genuinely useful to sellers (high adoption); nurture content passes PMM/brand QA; the propensity model is decision-useful and the human-in-the-loop guardrail is respected throughout.
Qualifications:
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.
Minimum Qualifications
- Be currently enrolled in a Bachelor's degree program and returning to the program after completion of the internship, graduating by June 2028
- Have general knowledge of core marketing frameworks and business operations concepts, with an emphasis on demand generation
- Have an interest in the B2B SaaS technology landscape with an eye on leveraging AI for marketing growth and effectiveness
- Be curious and passionate about your work
- Be able to commit to a full-time role (40 hours/week)
- Please note: At this time, we are only open to entry-level candidates with less than a year of professional experience (this does not include internships/co-ops)
Compensation
At Atlassian, we strive to design equitable and explainable compensation programs. To support this goal, the baseline of our range is higher than that of the typical market range, but in turn we expect to hire most candidates near this baseline. Base pay within the range is ultimately determined by a candidate's skills, expertise, or experience.
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: $43/hr – $51/hr
- Zone B: $39/hr – $44/hr
- Zone C: $36/hr – $43/hr
This role may also be eligible for benefits, bonuses, commissions, and equity.
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.
Benefits and Perks
Atlassian offers a wide range of perks and benefits designed to support you and 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 https://www.atlassian.com/company/careers/earlycareers.
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 with 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.
Salary Context
This $74K-$106K range is in the lower quartile 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 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 in Demand for This Role
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. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($90K) sits 57% below the category median. Disclosed range: $74K to $106K.
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.
Atlassian AI Hiring
Atlassian has 19 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, San Francisco, CA, US, Mountain View, CA, US. Compensation range: $106K - $309K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% 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 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
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