Senior Machine Learning Engineer, AI-Native Evaluation & Search
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What you'll do
- Build and productionize machine learning models and AI systems for Search & Intelligence products.
- Own projects from concept through deployment, monitoring, evaluation, and iteration.
- Develop AI-native experiences across search, retrieval, ranking, recommendations, conversational systems, and agentic workflows.
- Design evaluation-driven development processes, including quality metrics, test cases, benchmarks, and acceptance criteria.
- Build evaluation datasets, regression suites, and pipelines for search, RAG, chat, and agentic systems.
- Conduct offline and online evaluations, human assessments, model-based evaluations, and error analysis.
- Design scalable architectures that meet requirements for quality, reliability, latency, privacy, and cost.
- Apply modern techniques in information retrieval, ranking, embeddings, NLP, deep learning, and large language models.
- Communicate technical decisions and results clearly across technical and non-technical audiences.
- Mentor engineers and contribute to engineering and ML best practices.
What they're looking for
- A Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent experience.
- At least 4 years of experience developing and deploying machine learning or AI systems in production.
- Strong Python skills and experience with Java, Kotlin, TypeScript, or another production language.
- Experience with SQL and large-scale data processing technologies such as Spark.
- Experience building, evaluating, deploying, and scaling models with large datasets.
- Experience designing evaluation strategies, analyzing model quality, and using results to guide improvements.
- Familiarity with cloud-based ML and data platforms such as AWS or Databricks.
- Ability to independently solve ambiguous problems and deliver practical, production-quality solutions.
- Strong communication and collaboration skills.
- An agile mindset and commitment to continuous improvement.
Nice to have
- Experience building AI-native products, RAG systems, conversational assistants, or tool-using agents.
- Experience with evaluation-driven development, benchmarks, regression testing, human evaluation, or LLM-as-a-judge.
- Experience evaluating agent planning, tool use, task completion, or multi-step reasoning.
- Experience with search relevance, ranking, recommendations, personalization, embeddings, or NLP.
- Experience fine-tuning, post-training, evaluating, or optimizing large language models.
- Experience with ML platforms, model serving, data pipelines, observability, or responsible AI.
- Experience mentoring engineers or influencing technical direction within a team.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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
Build and productionize machine learning models and AI systems for Search & Intelligence products.
Own projects from concept through deployment, monitoring, evaluation, and iteration.
Develop AI-native experiences across search, retrieval, ranking, recommendations, conversational systems, and agentic workflows.
Design evaluation-driven development processes, including quality metrics, test cases, benchmarks, and acceptance criteria.
Build evaluation datasets, regression suites, and pipelines for search, RAG, chat, and agentic systems.
Conduct offline and online evaluations, human assessments, model-based evaluations, and error analysis.
Design scalable architectures that meet requirements for quality, reliability, latency, privacy, and cost.
Apply modern techniques in information retrieval, ranking, embeddings, NLP, deep learning, and large language models.
Communicate technical decisions and results clearly across technical and non-technical audiences.
Mentor engineers and contribute to engineering and ML best practices.
Qualifications:
On your first day, we’ll expect you to have
A Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent experience.
At least 4 years of experience developing and deploying machine learning or AI systems in production.
Strong Python skills and experience with Java, Kotlin, TypeScript, or another production language.
Experience with SQL and large-scale data processing technologies such as Spark.
Experience building, evaluating, deploying, and scaling models with large datasets.
Experience designing evaluation strategies, analyzing model quality, and using results to guide improvements.
Familiarity with cloud-based ML and data platforms such as AWS or Databricks.
Ability to independently solve ambiguous problems and deliver practical, production-quality solutions.
Strong communication and collaboration skills.
An agile mindset and commitment to continuous improvement.
It’s great, but not required, if you have
Experience building AI-native products, RAG systems, conversational assistants, or tool-using agents.
Experience with evaluation-driven development, benchmarks, regression testing, human evaluation, or LLM-as-a-judge.
Experience evaluating agent planning, tool use, task completion, or multi-step reasoning.
Experience with search relevance, ranking, recommendations, personalization, embeddings, or NLP.
Experience fine-tuning, post-training, evaluating, or optimizing large language models.
Experience with ML platforms, model serving, data pipelines, observability, or responsible AI.
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.
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.
Company
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