Principal Machine Learning Systems Engineer, Search Platform
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What you'll do
- Set the technical direction for a major ML-powered Search Data and indexing capability, translating customer and product problems into ambitious, measurable ML and systems outcomes.
- Advance and productionise ML approaches for document understanding, enrichment, embeddings and semantic representations, index construction and index evolution.
- Own the ML lifecycle end to end: data and feature strategy, model selection, adaptation or training, deployment, offline evaluation, monitoring and continuous improvement. Partner with Search Quality and product teams on online outcome measurement.
- Design scalable ML and distributed-data systems that operate across heterogeneous sources while balancing retrieval quality, freshness, correctness, reliability, latency and cost.
- Create reusable ML and indexing patterns that make content from new sources and content types easier to represent and index with strong default search quality.
- Lead difficult technical decisions across partner teams and advance the ML and engineering craft of the group through hands-on work, mentoring and clear communication.
What they're looking for
- 10+ years of hands-on experience building models or production ML systems, with deep applied-ML experience in areas such as search, information retrieval, recommendation, NLP, LLMs or content understanding.
- Proven experience designing, building and operating production ML systems across data preparation, representations or features, model development, deployment, evaluation, observability and model or data drift.
- Strong software, distributed-systems and data-engineering depth, including large-scale processing, indexing, failure recovery, schema evolution, scalability and cost.
- A track record of technical innovation: evaluating new research or industry techniques, proving their value through rigorous experiments and turning successful ideas into reliable production capabilities.
- Strong product judgment: ability to discover the real customer problem, define measurable outcomes and choose appropriately among rules, classical ML, deep learning and LLM-based approaches.
- Experience leading across ambiguous, multi-team initiatives, including setting direction, influencing senior engineers and developing other technical leaders.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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:
Search Platform’s mission is to power world-class, trusted cross-product knowledge search and discovery for people and agents across all of Atlassian’s product and agentic surfaces. We are looking for a product-minded ML systems leader with deep expertise in applied machine learning and production ML infrastructure.
As a Principal Machine Learning Systems Engineer, you will push the boundaries of how ML powers document understanding, content representations and indexing for enterprise and agentic search. You will own a major capability from problem framing and experimentation through architecture, launch, operation and continuous improvement.
Responsibilities
Set the technical direction for a major ML-powered Search Data and indexing capability, translating customer and product problems into ambitious, measurable ML and systems outcomes.
Advance and productionise ML approaches for document understanding, enrichment, embeddings and semantic representations, index construction and index evolution.
Own the ML lifecycle end to end: data and feature strategy, model selection, adaptation or training, deployment, offline evaluation, monitoring and continuous improvement. Partner with Search Quality and product teams on online outcome measurement.
Design scalable ML and distributed-data systems that operate across heterogeneous sources while balancing retrieval quality, freshness, correctness, reliability, latency and cost.
Create reusable ML and indexing patterns that make content from new sources and content types easier to represent and index with strong default search quality.
Lead difficult technical decisions across partner teams and advance the ML and engineering craft of the group through hands-on work, mentoring and clear communication.
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.
This role may also be eligible for benefits, bonuses, commissions, and equity.
Requirements
10+ years of hands-on experience building models or production ML systems, with deep applied-ML experience in areas such as search, information retrieval, recommendation, NLP, LLMs or content understanding.
Proven experience designing, building and operating production ML systems across data preparation, representations or features, model development, deployment, evaluation, observability and model or data drift.
Strong software, distributed-systems and data-engineering depth, including large-scale processing, indexing, failure recovery, schema evolution, scalability and cost.
A track record of technical innovation: evaluating new research or industry techniques, proving their value through rigorous experiments and turning successful ideas into reliable production capabilities.
Strong product judgment: ability to discover the real customer problem, define measurable outcomes and choose appropriately among rules, classical ML, deep learning and LLM-based approaches.
Experience leading across ambiguous, multi-team initiatives, including setting direction, influencing senior engineers and developing other technical leaders.
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.
Company
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