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Senior Machine Learning System Engineer

Seattle, WA

Pay
Multiple pay statements — pay source
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: $180,000 - $235,000 Zone B: $162,000 - $211,500
Zone A: $180,000 - $235,000 Zone B: $162,000 - $211,500 Zone C: $149,400 - $195,050
Zone B: $162,000 - $211,500 Zone C: $149,400 - $195,050 Qualifications
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Work setup
Unconfirmed
Employment
Unconfirmed
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What you’ll work on

Full posting
  • Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants.

  • Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses.

  • Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale.

From the employer’s posting
As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search experiences across Atlassian's product suite, including Jira, Confluence, and Rovo. Search Platform EngineeringDesign and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search. Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants. Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality. ML Model Development & ServingBuild and maintain production ML models including neural rankers, embedding models, and reranking systems. Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency. Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses.
Search Platform EngineeringDesign and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search. Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants. Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality. ML Model Development & ServingBuild and maintain production ML models including neural rankers, embedding models, and reranking systems. Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency. Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses. Agentic Search & RetrievalDesign retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows. Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale.
ML Model Development & ServingBuild and maintain production ML models including neural rankers, embedding models, and reranking systems. Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency. Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses. Agentic Search & RetrievalDesign retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows. Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale. Operational Excellence & Cost DisciplineDrive observability, monitoring, and incident response for search serving systems. Apply FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets. Maintain production health through rigorous on-call practices, runbook development, and proactive capacity planning.

Tools in this posting

  • PyTorch
Source — Tool mentions in context
Search Platform EngineeringDesign and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search. Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants. Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality. ML Model Development & ServingBuild and maintain production ML models including neural rankers, embedding models, and reranking systems. Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency. Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses. Agentic Search & RetrievalDesign retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows. Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale.

Benefits in the posting

Full benefits wording
  • 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.

From the employer’s posting.

About Atlassian

At Atlassian, we're motivated by a common goal: to unleash the potential of every team.

In the employer’s words · Read in context

Job description

View original posting ↗

Overview

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

As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search experiences across Atlassian's product suite, including Jira, Confluence, and Rovo.

Search Platform EngineeringDesign and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search. Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants. Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality.

ML Model Development & ServingBuild and maintain production ML models including neural rankers, embedding models, and reranking systems. Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency. Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses.

Agentic Search & RetrievalDesign retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows. Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale.

Operational Excellence & Cost DisciplineDrive observability, monitoring, and incident response for search serving systems. Apply FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets. Maintain production health through rigorous on-call practices, runbook development, and proactive capacity planning.

Cross-Functional CollaborationWork closely with engineering leads, product managers, and platform stakeholders to define technical roadmaps and deliver against team OKRs. Mentor junior engineers, contribute to design reviews, and champion engineering best practices across the team.

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: $180,000 - $235,000

Zone B: $162,000 - $211,500

Zone C: $149,400 - $195,050

Qualifications

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.

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Source & posting history

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Pay
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: $180,000 - $235,000 Zone B: $162,000 - $211,500
More source context
Zone A: $180,000 - $235,000 Zone B: $162,000 - $211,500 Zone C: $149,400 - $195,050

More relevant text appears in the full description.

Location & working pattern

Seattle, WA

Working pattern and location restrictions need checking in the full posting.

Work authorization

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First seen by us
Jul 14, 2026
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Last seen by us
Oct 8, 2026

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