1. What is a Machine Learning Engineer?

A Machine Learning Engineer at Atlassian builds and scales the systems that power intelligence across products like Jira, Confluence, and the broader Atlassian platform. You will turn messy, distributed product data into reliable ML services and user-facing AI features—search, recommendations, Q&A, summarization, abuse detection, and beyond. The work blends platform engineering with applied ML so that federated product teams can ship high-quality AI features quickly and safely.

Within Central AI and platform teams, you will design the foundations—data and model pipelines, curated access to LLMs, evaluation harnesses, and runtime services—to “democratize ML” across Atlassian. This role is critical because a single well-designed platform capability lifts dozens of product experiences simultaneously. In orgs like Trust Engineering, you’ll also apply ML to security, privacy, and anti-abuse at enterprise scale, where reliability, responsible AI, and compliance matter as much as model quality.

Expect to work at significant scale, with strict product SLA expectations, and with a pragmatic mindset. You will make trade-offs between performance, latency, cost, and iteration speed, all while aligning with Atlassian’s values: transparency, customer focus, and teamwork. If you enjoy building high-leverage ML platforms and shipping production AI features, this role gives you scope, complexity, and real user impact.

2. Getting Ready for Your Interviews

Approach preparation as a blend of backend coding, ML system design, and applied ML craft. The process favors candidates who can structure ambiguous problems, communicate clearly, and write production-quality code. Plan to demonstrate both platform thinking and practical, outcome-focused modeling.

Backend coding and code design – You will implement production-style components under ambiguity (e.g., rate limiter, parking system). Interviewers evaluate your OO design, data structures, correctness, and testability. Strength looks like clean APIs, thoughtful edge-case handling, and incremental delivery.

ML system design (MLSD) – You will design end-to-end ML solutions and LLM-based experiences for Atlassian use cases (search, Q&A, recommendations). Evaluation focuses on problem framing, metrics, data pipelines, offline/online evaluation, deployment, monitoring, and safety. Strong performance ties design choices to user and business outcomes with clear trade-offs.

Applied ML craft – You will deep-dive a past project and reason about modeling choices, experimentation, and measurable impact. Interviewers look for clarity about baselines, A/B testing, error analysis, and iteration speed. Strong candidates show how they reduced risk and improved metrics with principled experimentation.

Data and platform engineering – Expect discussion of AWS services (e.g., S3, SageMaker), microservices, CI/CD, and operating distributed systems. Strength looks like knowing how to build robust training and inference pipelines, optimize latency/cost, and set up monitoring and alerting.

Collaboration and values – You will be assessed on communication, stakeholder alignment, and handling ambiguity. Strong candidates surface assumptions, negotiate scope, and demonstrate ownership while embodying Atlassian values.

3. Interview Process Overview

Based on aggregated reports, Atlassian’s Machine Learning Engineer process typically starts with a recruiter screen, followed by a manager-led ML “craft” conversation, then multiple technical interviews covering backend coding, code design, and ML system design. Some teams run a fast-track or “elimination” first screen that combines a project deep dive and ML system design before inviting you to additional rounds. Pace can be brisk, but some candidates report pauses or slow decision loops in the current market.

Overall rigor is moderate to high, with an emphasis on real-world coding and end-to-end ML solutioning over academic theory. Coding problems often mirror backend components you would build for production ML services rather than algorithm puzzles. ML system design is central: you will be expected to reason about data, metrics, evaluation, and operational excellence. International candidates and those interviewing with Australian teams may also see values and business-case interviews.

Compared with other companies, the process is distinctive in two ways: a strong emphasis on backend production coding for an ML role, and a pragmatic approach to ML system design focused on user impact, platform leverage, and iterative delivery across federated product teams.

This visual lays out the typical sequence: recruiter/manager screens, technical screens that include ML craft and system design, backend coding/code design, and a final values/behavioral stage where applicable. Use it to time-box your preparation sprints and to sequence practice: first shore up coding, then run multiple MLSD mocks, and finally refine your project storytelling. Expect variations by team, level, and location, especially for Central AI vs. Trust Engineering.

4. Deep Dive into Evaluation Areas

Backend Coding and Low-Level Design

Backend implementation skill matters because Atlassian ships ML through services that must be correct, maintainable, and observable. Interviewers evaluate how you translate ambiguous requirements into clean APIs, choose appropriate data structures, and test incrementally. Strong performance looks like thoughtful decomposition, clear invariants, and production-quality code.

Be ready to go over:

• Object-oriented design and data structures – Maps/sets/queues, graphs, heaps, and when to choose each.
• Concurrency and rate control – Designing thread-safe components and rate limiters.
• API ergonomics and testability – Input validation, edge cases, and unit/integration tests.

Advanced concepts (less common):

• Efficient caching strategies, eviction policies, and consistency trade-offs.
• Backpressure, circuit breakers, and resilience patterns.
• Log-structured storage choices and read/write amplification considerations.

Example questions or scenarios:

• “Implement a simplified parking system with entry/exit, capacity, and pricing extensions.”
• “Build a snake-and-ladder game engine that supports variable board configurations.”
• “Design and code a token bucket rate limiter with burst handling and fairness.”

ML System Design

ML system design assesses your ability to deliver end-to-end impact for Jira/Confluence use cases. You will frame the problem, define success metrics, architect data and model pipelines, plan experimentation, and think about rollout, safety, and observability. Strong performance ties model choices to user experience, cost, latency, and reliability.

Be ready to go over:

• Problem framing and metrics – Choosing task definition and guardrail metrics aligned to product goals.
• Data architecture – Batch/stream ingestion, feature stores, labeling, and feedback loops.
• Deployment and monitoring – Canary, progressive rollout, drift detection, bias/safety checks.

Advanced concepts (less common):

• Retrieval-augmented generation, grounding, and prompt/adapter strategies for LLMs.
• Human-in-the-loop labeling, active learning, and offline/online metric alignment.
• Guardrails for responsible AI: output moderation, PII handling, and evaluation harnesses.

Example questions or scenarios:

• “Design a Confluence Q&A assistant that answers from internal pages with citations.”
• “Build Jira issue recommendations (assignees or related issues) with online feedback.”
• “Architect an LLM inference service that balances latency, cost, and quality across tenants.”

Applied ML Craft and Project Deep Dive

You will walk through a prior project to demonstrate practical ML judgment, experimentation discipline, and impact. Interviewers probe the why behind your choices, how you handled data issues, what baselines you used, and how you measured success. Strong performance shows clear hypotheses, strong baselines, and sharp error analysis leading to measurable gains.

Be ready to go over:

• Baseline-first thinking – Simple heuristics vs. complex models and why.
• Experimentation – A/B testing, power analysis, and confidence in results.
• Error analysis – Segment performance, fairness, and iteration prioritization.

Advanced concepts (less common):

• Counterfactual evaluation and off-policy estimators for recommenders.
• Calibration, uplift modeling, and long-horizon reward alignment.
• Causal inference to de-bias experiments.

Example questions or scenarios:

• “Walk us through an end-to-end recommender you built: objective, features, offline metrics, and A/B results.”
• “How did you debug a model whose offline AUC was high but online CTR did not improve?”
• “What trade-offs did you make to reduce inference latency without harming quality?”

Data Platform and MLOps on AWS

Many Atlassian roles require fluency in AWS and production ML tooling. Interviewers assess how you build, deploy, and operate pipelines and services with CI/CD, monitoring, and cost controls. Strong candidates navigate S3, SageMaker, microservices, and structured observability confidently.

Be ready to go over:

• Training and serving pipelines – Artifact versioning, feature lineage, and reproducibility.
• Runtime engineering – REST/gRPC services, autoscaling, caching, and circuit breaking.
• Observability – Metrics, logs, traces, and automated alerts for models and services.

Advanced concepts (less common):

• Multi-LLM routing, distillation, KV cache management, and inference optimization.
• Batch vs. streaming features and exactly-once semantics.
• Security, tenancy, and data governance in enterprise environments.

Example questions or scenarios:

• “Sketch a SageMaker training pipeline with data validation, model registry, and staged deployment.”
• “Design a multi-tenant inference service with per-tenant quotas and guardrails.”
• “How would you track and roll back a bad model using CI/CD and monitoring signals?”

Collaboration, Values, and Stakeholder Communication

You will be evaluated on how you handle ambiguity, align stakeholders, and live Atlassian values. Strong performance sounds like crisp problem statements, proactive risk surfacing, and collaborative decision-making. Expect scenario-based prompts where you negotiate scope, metrics, or timelines.

Be ready to go over:

• Communicating trade-offs between accuracy, latency, and cost.
• Aligning with PMs, design, and partner teams on requirements and success metrics.
• Handling conflicting feedback and deciding when to ship vs. iterate.

Advanced concepts (less common):

• Ethical considerations and responsible AI escalation paths.
• Running cross-team RFCs and design reviews efficiently.
• Mentoring peers on ML design and production standards.

Example questions or scenarios:

• “You’re asked to add a new feature one week before launch—how do you decide?”
• “A stakeholder wants a complex model; you believe a heuristic is sufficient—what do you do?”
• “How do you handle an ambiguous request for ‘AI in search’ without clear goals?”

This visualization highlights the topic mix across reported interviews—expect larger emphasis on backend coding, ML system design, AWS/MLOps, and LLM application patterns. Use it to prioritize prep time: double down on the largest topics, then allocate targeted sprints on smaller, differentiating areas. Reassess your plan if you see gaps in high-frequency themes.

5. Key Responsibilities

You will build and operate the core ML and AI capabilities that power Atlassian products and platform experiences. Day to day, you will design data and model pipelines, implement microservices that expose ML functionality, and create the evaluation and monitoring infrastructure to measure quality, safety, and business impact. You will collaborate closely with product engineers, data scientists, and PMs to scope problems, define success metrics, and ship iteratively.

On platform-oriented teams in Central AI, you will provide curated access to LLMs, implement retrieval and grounding, and deliver frameworks that product teams reuse. You will integrate with the Atlassian Data Platform, handle multi-tenant concerns, and ensure security and governance alignment. In Trust Engineering, you will focus on ML solutions for security, privacy, and abuse detection, emphasizing reliability, explainability, and compliance.

Expect to lead technical designs, author RFCs, review code, and mentor junior engineers. You will own production systems—on-call responsibilities, incident retrospectives, and continuous improvements are all part of the role.

6. Role Requirements & Qualifications

Successful Machine Learning Engineers at Atlassian blend strong software engineering with applied ML system design. You should be comfortable with AWS-based distributed systems, CI/CD, and operating ML services that meet user-facing SLAs. Experience with LLM deployment and inference optimization is increasingly valuable.

• Must-have skills

  • Proficiency in a modern OO language, ideally Java/Kotlin; strong Python for ML workflows.
  • ML lifecycle knowledge: data prep, training, evaluation, deployment, monitoring.
  • Backend engineering: RESTful microservices, concurrency, testing, observability.
  • AWS experience (e.g., S3, SageMaker, security/networking), CI/CD, infrastructure-as-code.
  • ML system design skills: problem framing, metrics, data architecture, rollout, and guardrails.

• Nice-to-have skills

  • LLMs in production: retrieval-augmented generation, prompt/adapter tuning, latency/cost optimization.
  • Feature stores, streaming pipelines, and experiment platforms.
  • Trust/Security domain exposure: detection systems, responsible AI, governance/compliance.
  • Mentoring, tech leadership, and cross-team platform enablement.

Typical backgrounds include ML engineers or software engineers with applied ML ownership in production systems. Senior roles expect leadership in ambiguous problem spaces and platform design.

7. Common Interview Questions

These questions are representative of patterns from candidate reports and may vary by team and level. Use them to guide your practice and to calibrate depth, not to memorize answers.

Backend Coding and Low-Level Design

Assesses ability to implement real services under ambiguity with clean design and correct data structures.

• Implement a parking system that supports different vehicle types and pricing extensions.
• Build a snake-and-ladder engine that supports variable board configuration and dice strategies.
• Design and code a token bucket rate limiter supporting bursts and thread safety.
• Format and count substrings/tokens in a large text stream under memory constraints.
• Implement an in-memory cache with TTL and eviction policy; discuss complexity and tests.

ML System Design

Evaluates end-to-end thinking from data to deployment, metrics, and operations.

• Design a Confluence Q&A assistant that answers from internal pages with citations and guardrails.
• Build Jira issue recommendations with online learning and feedback loops; define metrics and rollout.
• Architect an LLM inference service with multi-model routing and cost/latency/quality trade-offs.
• Propose an evaluation plan for a search relevance improvement across products.
• Design a drift detection and auto-rollback mechanism for a classification service.

Applied ML Craft and Modeling

Tests depth on past work, experimentation rigor, and practical trade-offs.

• Walk through a recommender you shipped: baselines, features, offline metrics, and A/B outcomes.
• How did you diagnose a model that performed well offline but failed to move a business KPI?
• Describe a time you improved latency at inference without sacrificing quality—what changed?
• Explain your error analysis workflow and how it informed your roadmap.
• Discuss how you managed label noise and sampling bias in a past project.

MLOps and Platform on AWS

Focuses on building, deploying, and operating ML systems with reliability.

• Sketch a training-to-serving pipeline on AWS with versioning, model registry, and staged deploys.
• How would you structure observability for an ML microservice? Metrics, logs, traces, and alerts.
• Design a multi-tenant LLM gateway with per-tenant quotas, safety filters, and isolation.
• Discuss cost optimization strategies for high-throughput inference.
• How do you ensure reproducibility and governance for experiments?

Behavioral, Collaboration, and Values

Assesses communication, ownership, and alignment with Atlassian values.

• Tell us about a time you simplified a complex ML solution to ship faster with similar impact.
• Describe a situation where stakeholders disagreed on metrics—how did you drive alignment?
• How do you handle ambiguous product asks like “add AI to search”?
• Give an example of mentoring an engineer through a design review or incident.
• Share a time you identified a risk in production ML and addressed it proactively.

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

8. Frequently Asked Questions

Q: How difficult is the interview, and how much time should I prepare?
Most candidates rate difficulty as easy-to-medium, but the bar for clarity and production readiness is real. Plan 3–4 weeks of focused prep: 2 weeks for backend coding/LLD and 1–2 weeks for ML system design and project storytelling.

Q: What differentiates successful candidates?
They structure ambiguous problems quickly, write clean and testable code, and tie ML design to user and business outcomes. They also show pragmatic trade-offs, a strong handle on evaluation, and comfort operating in AWS-based production environments.

Q: What is the typical timeline?
Expect an initial screen, a craft/manager conversation, 2–3 technical interviews, and possibly a final values/business case session. Timelines can vary by team and market conditions; some candidates report pauses after screens.

Q: Is this role remote or hybrid?
Atlassian is distributed-first and hires in locations with a legal entity. Confirm expectations with your recruiter, especially for your region and team.

Q: How should international candidates approach the process?
Clarify work authorization early and confirm the role/level to avoid misalignment. Provide timelines and any constraints upfront so scheduling and team matching proceed smoothly.

9. Other General Tips

• Anchor on user impact early: In MLSD, open with user problem, constraints, and success metrics before proposing architecture. This sets a product-centric frame.
• Make ambiguity your ally: Ask clarifying questions; propose a v1 and an evolution path. Interviewers want to see how you reduce uncertainty and ship.
• Code like production: Favor clear abstractions, input validation, tests, and incremental correctness. Narrate complexity choices and trade-offs.
• Instrument your solutions: In design interviews, specify logs/metrics/traces and what alerts you would set. Tie monitoring to rollback criteria.
• Own evaluation: Define offline metrics, online KPIs, guardrails, and a phased rollout. Explain how you’d debug offline–online deltas.

• Pre-bake a project deep dive: Prepare a 5–7 minute narrative with diagrams, baselines, experiments, impact, and lessons learned. Anticipate probing questions on data quality and trade-offs.

• Close with iteration plans: End designs with a v2 roadmap—quality improvements, cost reductions, and safety/guardrail enhancements that you’d prioritize next.

10. Summary & Next Steps

The Machine Learning Engineer role at Atlassian is a high-impact opportunity to build platform capabilities and ship AI features across products used by millions. You will operate at the intersection of robust backend engineering, ML system design, and pragmatic applied ML, often with LLMs and AWS-based infrastructure. The problems are real, the stakes are high, and the leverage is company-wide.

Focus your preparation on four themes: production-grade backend coding and LLD; end-to-end ML system design with crisp metrics and rollout plans; applied ML craft through project deep dives; and MLOps fundamentals on AWS. Practice with realistic, product-flavored problems and emphasize trade-offs, observability, and responsible AI throughout. Consistent, deliberate preparation materially improves performance.

Explore additional interview insights and resources on Dataford to complement this guide. Approach each round with clarity, structure, and ownership—you can absolutely excel here with focused prep and a product mindset.

This module summarizes compensation ranges by geographic zone and level indications pulled from recent postings. Use it to calibrate your expectations and to frame compensation discussions based on your location, experience, and scope. Remember that total compensation may include equity, bonuses, and benefits; confirm details with your recruiter during later stages.