Senior Java Software Engineer - Search Inference - Platform

Elastic
Full-time•Platform / Infrastructure•Ireland
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At a glance

Employment type
Full-time
Location
Ireland
Workplace
On-site

Technologies & skills

Primary technologies

Other technical skills

What you'll be doing

Elastic seeks a Senior Java Software Engineer to join the Elasticsearch team, focusing on the Inference API. The role involves developing and maintaining integrations with internal and external inference providers, contributing to plugin architecture, reviewing open source contributions, and participating in on-call rotations.

  • Build and maintain integrations with internal systems and external inference providers, including schema negotiation, streaming (SSE), and error normalisation
  • Contribute to the inference plugin's internal architecture: task settings, chunking strategies, rate limiting, and provider-level configuration
  • Review open source contributions to the codebase and work with third party engineers to ensure their contributions meet technology standards
  • Participate in on-call rotation for inference-related incidents
  • Write thorough tests: unit tests using ESTest, YAML-based REST integration tests, and rolling-upgrade tests

Key requirements

Must-have

  • 5+ years of production Java experience
  • Experience contributing to a large, established Java codebase
  • Knowledge of core Java, standard library data structures, concurrency constructs, and newer language features
  • Experience crafting ergonomic APIs and reasoning through tradeoffs
  • Experience with low-level Java (concurrency, parallelism, classloaders, etc.)
  • Proven track record of using AI to accelerate development, debug complex systems, and optimize code
  • Ability to collaborate across functions and teams
  • Ability to work autonomously and drive decisions in a distributed team

Nice-to-have

  • Knowledge of JDK internals (modules, Java memory model, garbage collection, database load, caching techniques)
  • Experience with ML systems
  • Contributions to and ownership of open source projects
  • Knowledge of Java build tools (Gradle, Maven), with preference for Gradle
  • Experience working in a platform or developer productivity team

Experience: 5+ years of production Java experience

Role signals

Technical focus
Platform / Infrastructure
Architecture / system design
Indicated in the listing
Hands-on vs management
Hands-on

Full job description

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.

What is The Role

We are on the lookout for a Senior Java Developer to join our Elasticsearch team and drive our work on the Inference API in Elasticsearch.

The Elasticsearch Inference API is the integration layer between Elasticsearch and external machine learning services including Elastic's own Elastic Inference Service (EIS), third-party providers (Anthropic, OpenAI, Cohere, Google), and self-hosted models. It powers features like semantic search, reranking, and generative AI across Elastic Cloud and in hosted environments.

You'll maintain and develop the plugin framework and REST API with your work spanning protocol design, streaming, async execution, billing integration, and with a tight collaboration with the wider inference team and the Kibana team.

What You Will Be Doing

  • Build and maintain integrations with internal systems and external inference providers, including schema negotiation, streaming (SSE), and error normalisation

  • Contribute to the inference plugin's internal architecture: task settings, chunking strategies, rate limiting, and provider-level configuration

  • Review open source contributions to the codebase and work with third party engineers to ensure their contributions meet our technology standards

  • Participate in on-call rotation for inference-related incidents

  • Write thorough tests:

  • unit tests using ESTest

  • Case, YAML-based REST integration tests, and rolling-upgrade tests

What You Bring

  • 5+ years of production Java experience, with experience contributing to a large, established Java codebase. You know core Java and are conversant in the standard library of data structures and concurrency constructs, as well as newer language features.

  • Experience crafting ergonomic APIs and the ability to reason through tradeoffs.

  • Experience with low-level Java such as concurrency, parallelism, classloaders, etc.

  • You have a proven track record of using AI to accelerate development, debug complex systems, and optimize code, while still owning the final outcomes.

  • You possess the ability to collaborate across functions and teams and seamlessly transition between different projects, codebases, or teams based on business priorities

  • You can work autonomously, drive decisions and results in a distributed team by leveraging asynchronous, direct, and transparent communication.

Bonus Points

  • Knowledge of JDK internals:

  • modules, the

  • Java memory model, garbage collection, database load, caching techniques, etc.

  • Experience with ML systems

  • Contributions to and ownership of open source projects

  • Knowledge of Java build tools (Gradle, Maven, etc), with a strong preference for Gradle experience.

  • Experience working in a platform team or developer productivity team

Compensation for this role is in the form of base salary. This role does not have a variable compensation component.

At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets.

The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position.

The typical starting salary range for this role is:

€87.300—€138.100 EUR

Additional Information - We Take Care of Our People

As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do.

We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do.

  • Competitive pay based on the work you do here and not your previous salary

  • Health coverage for you and your family in many locations

  • Ability to craft your calendar with flexible locations and schedules for many roles

  • Generous number of vacation days each year

  • Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service

  • Up to 40 hours each year to use toward volunteer projects you love

  • Embracing parenthood with minimum of 16 weeks of parental leave

Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation.

We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission.

Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster)

Elasticsearch develops and distributes technology and information that is subject to U.S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic.

Please see here for our Privacy Statement.

Interview prep pack

Grounded in this listing. Use it to prepare examples before you apply.

Your interview focus

Based on this listing, the Senior Java Software Engineer - Search Inference - Platform role at Elastic focuses on developing and maintaining the Elasticsearch Inference API, integrating with external and internal machine learning services, and contributing to the architecture and reliability of the inference plugin.

  • Java expertise in large, distributed systems·High
  • API and plugin architecture·High
  • Integration with ML/AI providers·Medium
  • Testing and code quality·Medium

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review Elasticsearch Inference API and Plugin Documentation

    0–8 min

    Spend time understanding the architecture, integration points, and plugin framework of the Elasticsearch Inference API.

  2. Refresh Advanced Java Concepts

    8–15 min

    Revisit concurrency, parallelism, classloaders, and recent Java language features relevant to distributed systems.

  3. Prepare Examples of API/Plugin Design and Integration

    15–21 min

    Select and outline 1-2 strong examples from your experience designing APIs or integrating with external ML/AI services.

  4. Study Testing Approaches for Distributed Java Systems

    21–26 min

    Review best practices and tools for unit, integration, and rolling-upgrade testing in distributed environments.

  5. Draft Questions and Review Collaboration Practices

    26–30 min

    Prepare thoughtful questions for the interview and reflect on your experience collaborating in distributed or open source teams.

Likely questions

, 7 items

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

Talking points

, 5 items
  • Deep Java Expertise in Large Codebases

    You will need to demonstrate experience working with complex, established Java systems, including concurrency, parallelism, and classloaders. Prepare examples where you navigated or improved large-scale Java projects.

  • API and Plugin Framework Design

    The role requires crafting ergonomic APIs and maintaining plugin frameworks. Be ready to discuss design decisions, tradeoffs, and how you ensured usability and maintainability.

  • Integration with ML/AI Services

    You will build and maintain integrations with various inference providers. Prepare to discuss previous experience integrating with external APIs, handling schema negotiation, streaming, and error normalization.

  • Testing Strategies for Distributed Systems

    Thorough testing is emphasized, including unit, integration, and rolling-upgrade tests. Be prepared to explain your approach to testing in distributed or cloud-based environments.

  • Collaboration and Code Review

    The role involves reviewing open source contributions and working with third-party engineers. Prepare examples of effective collaboration, code review, and maintaining high standards in a distributed team.

What to research

, 4 items
  • Elasticsearch Inference API and Plugin Architecture

    Review the architecture and documentation of the Elasticsearch Inference API, focusing on how it integrates with external ML providers and the plugin framework.

  • Advanced Java Features and Best Practices

    Refresh your knowledge of Java concurrency, parallelism, classloaders, and recent language features relevant to large-scale, distributed systems.

  • Testing Strategies in Distributed Systems

    Study approaches to unit, integration, and rolling-upgrade testing in distributed Java applications, including tools like ESTest and YAML-based REST tests.

  • Integration Patterns with External ML/AI Services

    Research common patterns for integrating with third-party inference providers (e.g., OpenAI, Anthropic), including schema negotiation, streaming, and error handling.

Questions to ask

, 6 items
  1. How does the team prioritize new integrations with external inference providers?

    Why ask this? To understand how business needs and technical feasibility drive roadmap decisions.

  2. What are the main challenges currently faced in maintaining the inference plugin framework?

    Why ask this? To identify technical pain points and areas where your expertise could add value.

  3. How is collaboration structured between the inference team and other teams like Kibana?

    Why ask this? To clarify cross-team workflows and communication expectations.

  4. What is the team's approach to code review, especially for open source contributions?

    Why ask this? To gauge the code quality standards and your potential role in maintaining them.

  5. How does the on-call rotation work for inference-related incidents?

    Why ask this? To understand operational responsibilities and expectations for incident response.

  6. What opportunities exist for contributing to or leading architectural decisions within the team?

    Why ask this? To assess your potential influence on technical direction and growth.

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Apply NowApply before: 6 Nov 2026