Senior Machine Learning Engineer, AI-Native Evaluation & Search
San Francisco, CAJob$206–269K/yrPosted 2w agoStill listed today
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Job overview
Atlassian seeks a Senior Machine Learning Engineer for its Search & Intelligence organization, building AI-native experiences, agentic systems, models, evaluation frameworks, and data platforms. The role develops and productionizes machine learning systems across search, retrieval, ranking, conversational experiences, and AI agents, while owning work across the ML lifecycle. It also emphasizes evaluation-driven development, cross-functional collaboration, technical direction, and mentoring.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
Overview
Atlassian is looking for a Senior Machine Learning Engineer to join our Search & Intelligence organization. We build AI-native experiences, agentic systems, models, evaluation frameworks, and data platforms that power Atlassian’s AI products.
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.
Responsibilities
Your role and impact You will develop and productionize machine learning systems for search, retrieval, ranking, conversational experiences, and AI agents. You will own projects across the ML lifecycle, from problem definition and data development through experimentation, evaluation, deployment, and continuous improvement. A key part of this role is making evaluation a first-class part of AI development. You will help define quality, build evaluation datasets and benchmarks, analyze model behavior, and use results to guide product and engineering decisions. You will collaborate with product managers, software engineers, data scientists, research scientists, and platform teams. You will independently solve complex problems, contribute to technical direction, and mentor other engineers.
What you’ll do
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Build and productionize machine learning models and AI systems for Search & Intelligence products.
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Own projects from concept through deployment, monitoring, evaluation, and iteration.
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Develop AI-native experiences across search, retrieval, ranking, recommendations, conversational systems, and agentic workflows.
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Design evaluation-driven development processes, including quality metrics, test cases, benchmarks, and acceptance criteria.
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Build evaluation datasets, regression suites, and pipelines for search, RAG, chat, and agentic systems.
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Conduct offline and online evaluations, human assessments, model-based evaluations, and error analysis.
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Design scalable architectures that meet requirements for quality, reliability, latency, privacy, and cost.
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Apply modern techniques in information retrieval, ranking, embeddings, NLP, deep learning, and large language models.
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Communicate technical decisions and results clearly across technical and non-technical audiences.
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Mentor engineers and contribute to engineering and ML best practices.
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: $206,100 - $269,075 Zone B: $185,490 - $242,168 Zone C: $171,063 - $223,332
Qualifications
On your first day, we’ll expect you to have
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A Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent experience.
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At least 4 years of experience developing and deploying machine learning or AI systems in production.
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Strong Python skills and experience with Java, Kotlin, TypeScript, or another production language.
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Experience with SQL and large-scale data processing technologies such as Spark.
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Experience building, evaluating, deploying, and scaling models with large datasets.
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Experience designing evaluation strategies, analyzing model quality, and using results to guide improvements.
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Familiarity with cloud-based ML and data platforms such as AWS or Databricks.
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Ability to independently solve ambiguous problems and deliver practical, production-quality solutions.
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Strong communication and collaboration skills.
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An agile mindset and commitment to continuous improvement.
It’s great, but not required, if you have
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Experience building AI-native products, RAG systems, conversational assistants, or tool-using agents.
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Experience with evaluation-driven development, benchmarks, regression testing, human evaluation, or LLM-as-a-judge.
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Experience evaluating agent planning, tool use, task completion, or multi-step reasoning.
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Experience with search relevance, ranking, recommendations, personalization, embeddings, or NLP.
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Experience fine-tuning, post-training, evaluating, or optimizing large language models.
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Experience with ML platforms, model serving, data pipelines, observability, or responsible AI.
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Experience mentoring engineers or influencing technical direction within a team.
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.
How this job compares
Set against the 6,060 open Machine Learning Engineer jobs on Olive Jobs, counted November 5, 2026.
- Pay
- This posting$206–269K/yr
- California median$177–261K/yr1,365 salary ranges
- US median$150–245K/yr4,818 salary ranges
This range's midpoint, $238K, is 8% above the California median midpoint ($221K) — in the middle half of Machine Learning Engineer salary ranges in California.
- Openings
- Olive Jobs lists 1,838 open Machine Learning Engineer jobs in California and 6,060 across the US. 8% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 4 years of experience; Machine Learning Engineer postings that state a minimum ask for 5 years at the median (4,865 postings).
- Education
- 2,211 of the 6,060 open Machine Learning Engineer jobs (36%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 5, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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