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Senior machine learning engineer

Mountain View, CA

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: $206,100 - $269,075 Zone B: $185,490 - $242,168
Zone A: $206,100 - $269,075 Zone B: $185,490 - $242,168 Zone C: $171,063 - $223,332
Zone B: $185,490 - $242,168 Zone C: $171,063 - $223,332 Qualifications
Read the full posting
Work setup
Unconfirmed
Employment
Unconfirmed
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What you’ll work on

Full posting
  • Build and deliver agentic search capabilities for AI assistants and developer workflows through APIs, command-line tools, and protocols such as the Model Context Protocol (MCP).

  • Develop reproducible benchmarks and evaluation tools that reflect realistic tasks across models and agent environments.

  • Build agentic search systems that interpret user intent, plan and execute searches, select appropriate sources and tools, and adapt based on retrieved evidence.

From the employer’s posting
What you’ll do Build and deliver agentic search capabilities for AI assistants and developer workflows through APIs, command-line tools, and protocols such as the Model Context Protocol (MCP). Develop reproducible benchmarks and evaluation tools that reflect realistic tasks across models and agent environments. Enable trustworthy performance comparisons, make failures easier to diagnose, and accelerate experimentation.
Build and deliver agentic search capabilities for AI assistants and developer workflows through APIs, command-line tools, and protocols such as the Model Context Protocol (MCP). Develop reproducible benchmarks and evaluation tools that reflect realistic tasks across models and agent environments. Enable trustworthy performance comparisons, make failures easier to diagnose, and accelerate experimentation. Use evaluation results, production signals, and developer feedback to improve the full search system—from search strategies and model behavior to tool interfaces, input and output schemas, and context selection.
Use evaluation results, production signals, and developer feedback to improve the full search system—from search strategies and model behavior to tool interfaces, input and output schemas, and context selection. Build agentic search systems that interpret user intent, plan and execute searches, select appropriate sources and tools, and adapt based on retrieved evidence. Explore and apply advances in LLMs and agent systems, including prompting, model selection, training data improvements, and fine-tuning where appropriate. Use evidence to decide which approaches to bring into production.
Education & alternatives
On the first day, we’ll expect you to have - A bachelor’s or master’s degree in Computer Science or a related field, or equivalent practical experience. - 4+ years of relevant industry experience in machine learning, including delivering ML capabilities into production.

Tools in this posting

  • Python
  • AWS
  • Databricks
Source — Tool mentions in context
- 4+ years of relevant industry experience in machine learning, including delivering ML capabilities into production. - Strong Python programming skills and the ability to build reliable, maintainable production systems. - Experience in one or more of LLM applications, AI agents, information retrieval, search relevance, or natural language processing.
- Experience with LLM fine-tuning or post-training, including supervised fine-tuning, preference optimization, or reinforcement learning. - Experience with distributed data processing and cloud ML environments such as Spark, AWS, or Databricks. Compensation

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

Senior Machine Learning Engineer — Agentic Search

Atlassian is seeking a Senior Machine Learning Engineer to join our Agentic Search team. You’ll build agentic search systems that help people and AI agents discover relevant enterprise knowledge, reason through complex questions, and accomplish tasks across products and tools.

Your future team

Our team is part of Search Relevance at Atlassian. We bring enterprise search into the places where people and agents work, including Atlassian products, third-party AI assistants, and developer workflows.

We build systems that interpret intent, plan searches, use tools, and adapt as new evidence becomes available. Rigorous evaluation guides our development: we benchmark realistic tasks, investigate failures, and use what we learn to improve the full search experience—from agent behavior to the interfaces and information we provide.

We combine applied research with production engineering, working closely with product, search infrastructure, modeling, and evaluation teams. We use AI tools throughout our daily work to prototype, build, evaluate, and learn, and continually evolve our methods as the technology advances.

What you’ll do

  • Build and deliver agentic search capabilities for AI assistants and developer workflows through APIs, command-line tools, and protocols such as the Model Context Protocol (MCP).

  • Develop reproducible benchmarks and evaluation tools that reflect realistic tasks across models and agent environments. Enable trustworthy performance comparisons, make failures easier to diagnose, and accelerate experimentation.

  • Use evaluation results, production signals, and developer feedback to improve the full search system—from search strategies and model behavior to tool interfaces, input and output schemas, and context selection.

  • Build agentic search systems that interpret user intent, plan and execute searches, select appropriate sources and tools, and adapt based on retrieved evidence.

  • Explore and apply advances in LLMs and agent systems, including prompting, model selection, training data improvements, and fine-tuning where appropriate. Use evidence to decide which approaches to bring into production.

  • Own projects from problem definition and technical design through experimentation, deployment, and ongoing measurement, building reliable systems that respect enterprise permissions and data boundaries.

  • Collaborate across product, search, and AI teams to shape technical direction and integrate agentic search into customer experiences.

  • Contribute to technical design and code reviews, mentor engineers, and help the team evolve its engineering practices, including effective use of AI tools.

Your background

On the first day, we’ll expect you to have

  • A bachelor’s or master’s degree in Computer Science or a related field, or equivalent practical experience.

  • 4+ years of relevant industry experience in machine learning, including delivering ML capabilities into production.

  • Strong Python programming skills and the ability to build reliable, maintainable production systems.

  • Experience in one or more of LLM applications, AI agents, information retrieval, search relevance, or natural language processing.

  • Experience designing experiments, building evaluation datasets, and analyzing model and system behavior to guide improvements.

  • Regular use of AI tools in your daily engineering workflow, such as coding, prototyping, debugging, or experimentation, with the judgment to validate their outputs and take ownership of the results.

  • An understanding of the ML development lifecycle, from data preparation and modeling to deployment, monitoring, and iteration.

  • The ability to lead ambiguous technical projects, learn new approaches quickly, make practical tradeoffs, and communicate clearly with engineering and product partners.

It’s great, but not required, if you have

  • Experience building agents that use tools, multi-step search systems, or retrieval-augmented generation applications.

  • Experience designing APIs, developer tools, or interfaces that make search and knowledge accessible to AI agents, including MCP.

  • Experience developing agent evaluations or reproducible benchmarks, including trajectory analysis, human evaluation, or model-based judging.

  • Experience with semantic or hybrid retrieval, ranking, or context-aware search.

  • Experience with LLM fine-tuning or post-training, including supervised fine-tuning, preference optimization, or reinforcement learning.

  • Experience with distributed data processing and cloud ML environments such as Spark, AWS, or Databricks.

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

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.

Your next step

  • Have your CV and examples of relevant work ready.
  • Check the listed location, eligibility and core experience before starting.

Complete your application on careers-americas.icims.com. The employer’s form will show what is required.

Already applied? Track this application

Source & posting history

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Source notes

Source excerpts

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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: $206,100 - $269,075 Zone B: $185,490 - $242,168
More source context
Zone A: $206,100 - $269,075 Zone B: $185,490 - $242,168 Zone C: $171,063 - $223,332

More relevant text appears in the full description.

Location & working pattern

Mountain View, CA

- Experience developing agent evaluations or reproducible benchmarks, including trajectory analysis, human evaluation, or model-based judging. - Experience with semantic or hybrid retrieval, ranking, or context-aware search. - Experience with LLM fine-tuning or post-training, including supervised fine-tuning, preference optimization, or reinforcement learning.
Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

Status in our records
Active
First seen by us
Oct 1, 2026
Recorded sightings
13
Last seen by us
Oct 8, 2026

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