Staff Engineer - Search
Staffbase is hiring a Staff Engineer - Search in Berlin, Germany. Level rates it ; you can apply on Staffbase.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions Kotlin, Java and Opensearch among its tools and skills.
- Level classes it as a senior role.
- Staffbase has 27 other open roles on Level.
- Posted in the last few days.
AI in this role
Staff Engineer for Search to lead hybrid retrieval, ranking, and AI-driven grounded answer systems across the employee platform.
About Staffbase
We inspire people to achieve great things together. Our mission is to help organizations unlock the power of inspirational communication with the first AI-native Employee Experience Platform. Our industry-leading and award-winning agentic AI communications channels - intranet, employee app and email solutions - create engaging experiences that connect and empower employees.
Headquartered in Chemnitz, Germany and New York City, with offices in Berlin, London, Sydney, Tokyo, Prague, and Minneapolis–St. Paul, our diverse team of 550+ employees supports 1,500+ customers—reaching over 14 million employees—in transforming their employee experience.
We are proud to be a Unicorn company—privately valued at over $1 billion—demonstrating strong growth, innovation, and lasting impact in our industry. Together, we’re shaping the future of workplace communication.
As a Staff Engineer on the Search team, you'll take on one of our most important platform challenges: making every employee able to find what they need, and get a trustworthy answer, across Staffbase and the tools around it. Today search spans people, posts, pages, files and connected integrations, and it increasingly feeds AI-generated answers. You'll define how retrieval, ranking and grounded answers work at that scale.
Your north star is a search platform that finds the right thing, fast, for the right person. It must respect permissions on every query, work in every language our customers use, and give answers that cite their sources. You'll build the core, set the quality bar, and decide where we use proven techniques and where we experiment, all on a mature, multi-tenant platform.
This is a role for someone who wants to drive, not just deliver. You'll shape the direction of an open-ended problem, and you'll measure your work with real relevance data, not gut feeling.
Our tech stack includes Kotlin, Java, OpenSearch/Elasticsearch, MongoDB, React, TypeScript, Terraform, Docker and Playwright, plus LLM and embedding services for retrieval, reranking and summarisation.
The Team
The Search team sits at the heart of how employees find information at Staffbase. We own the full path from query to result: indexing and embeddings, hybrid keyword and semantic retrieval, ranking and reranking, typeahead, and the AI summaries that answer questions with citations. Search is also a core source of knowledge for Navigator, our AI assistant, and the main way it grounds its answers in company content.
That means two big things. First, we make results relevant: understanding the query, retrieving the right candidates, ranking them well, and doing it in the user's language. Second, we make search safe and fast: every result respects the user's access rights, every tenant stays isolated, and the fast path stays fast. We don't ship relevance changes on a hunch. We run offline evaluations, golden query sets and online signals, and we change one thing at a time.
Our environment
- Working with skilled and similar-minded professionals in a team that values diversity and inclusivity
- We aim to create a flexible and agile work environment that promotes the well-being and work-life balance of our employees
- We are enthusiastic about technology and committed to using it to improve the world
- We follow the "You build it, you run it" approach and take pride in it
What you'll be doing
- Define and drive the search and retrieval strategy: hybrid (BM25 + vector) retrieval, reranking, query understanding and rewriting, chunking and embedding choices
- Build and evolve the core ingestion and query pipeline, from sync and indexing to ranked results and AI summaries with citations
- Own search quality: build evaluation tooling and golden sets (including unanswerable and permission-varying queries), define relevance metrics, and turn them into decisions
- Protect the non-negotiables at scale: permission-aware retrieval, tenant isolation, multilingual handling, and graceful degradation when a dependency is slow or down
- Balance latency, quality and cost, for example what runs on the fast path versus what is allowed on the slow path
- Make search observable: tracing, relevance dashboards, and a clear way to debug why a result did or did not appear
- Lead technical strategy and architecture, and document key decisions so others can build on them
- Take ownership of major technical decisions, proactively addressing bottlenecks and ensuring product quality
- Work with Product and Design to turn user problems into measurable search improvements
- Contribute to technical roadmaps with engineering leadership, and coordinate with teams that feed or consume search (content, integrations, Navigator)
- Mentor and guide engineers through code reviews, design discussions and experiment reviews
- Foster a culture of ownership, technical excellence and continuous improvement with teams that feed or consume search (content, integrations, Navigator)
What you need to be successful
- Proven track record as a Staff Engineer or Technical Lead with strong leadership and problem-solving skills
- Proven experience building production search or retrieval systems (OpenSearch, Elasticsearch, Solr or similar), including relevance tuning, analyzers, and index design
- Hands-on experience with modern retrieval: vector/kNN search, hybrid fusion (e.g. RRF), cross-encoder reranking, chunking and embeddings; ideally RAG or LLM-grounded answers
- A measurement mindset: you've built or used relevance evaluation (recall, MRR, nDCG, golden sets, LLM-as-judge with human checks) and can tell a real improvement from noise
- Strong understanding of multi-tenant architecture and permission-aware retrieval, with good judgment on security and isolation in search
- Experience with latency-sensitive systems: budgets, timeouts, caching, fallbacks and degradation
- Extensive experience with Kotlin/Java (or Go) on the backend; comfortable with React/TypeScript on the frontend
- Knowledge of architecture and design patterns, and the judgment to make trade-offs and weigh their long-term impact
- Strong communication skills, able to explain relevance and ranking trade-offs to technical and non-technical stakeholders
- Experience working with Product and Design leaders to deliver customer value in an Agile environment
- Passion for building high-quality software
- Passion for diversity and inclusion, and the ability to foster a culture where everyone's voice is heard
Nice to have
- Multilingual search (analyzers, language detection, cross-language retrieval)
- Query understanding: rewriting, expansion, typeahead and spell correction
- Prompt-injection and data-poisoning awareness in RAG systems
- Experience with LLM observability and evaluation tooling
What you'll get
- Competitive Compensation - we offer attractive salary packages including LTIP (unit-based Long Term Incentive Plan)
- Flexibility - we offer flexible working time models and the option of hybrid work, and support this with a yearly flex work allowance of €1560
- Recharge - with 31 vacation days annually (incl. one floating holiday), plus pro rata fully paid Fridays off during August
- Support - we offer offering a company pension scheme
- Volunteers Day - you’ll get one day off per year for supporting a social project
How we rate this
Staff Engineer - Search at Staffbase rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Tell me about a project where search engineering was part of your work. What did you do?
- Tell me about a project where retrieval augmented generation was part of your work. What did you do?
- Tell me about a project where ranking was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, Search Engineering, Retrieval Augmented Generation, Ranking, and Distributed Systems. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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