[Hiring] AI Engineer (Search & Matching) @JobLeads Careers

Role Description

As an Applied AI Engineer, you will build the intelligence behind how candidates and jobs find each other: semantic search, resume-based matching, LLM-driven query understanding, and recommendations. The role evolves with the mission: whatever it takes to make our search the best job search on the internet is what you'll work on next.

That makes out-of-the-box thinking a feature of the job, not a nice-to-have. We expect you to bring new approaches, methods, and frameworks to the table, whether they come from commercial model providers or straight from current research.

What stays constant is the way of working:

• Form a hypothesis, benchmark it rigorously against the status quo.
• Make it tangible in a prototype people can play with.
• Ship the winner to production and prove it in an A/B test.
• Work closely with decision-makers and present your results directly.

What You'll Be Doing:

• Design and improve retrieval systems over a large, messy real-world corpus at scale: embeddings, hybrid search, ranking, and re-ranking.
• Shape the design of our vector search backbone: how jobs and resumes are represented, which embedding models to use, the right vector dimensionality for each purpose, etc.
• Build LLM pipelines for understanding queries, resumes, and jobs — extraction, enrichment, rewriting, matching - choosing the right model and technique for each step.
• Own evaluation as a scientific discipline: curate benchmark datasets that capture both the failure modes you're fixing and the healthy cases you must not regress.
• Design LLM-as-judge setups you can defend - ground and calibrate the judge against human sanity checks, detect its biases, and iterate on the judge itself when it measures the wrong thing.
• Apply and adapt search-relevance methodology - precision@k, NDCG, pairwise preference testing - choosing metrics that resist gaming, and recognizing when a metric rewards degenerate behavior.
• Read results like a scientist: cumulative vs. independent effects, why two valid metrics can disagree, what a distribution's tail says that its mean hides.
• Quantify the full picture - quality, latency percentiles, and cost per query.
• Build interactive prototypes so product managers and stakeholders can test your approaches hands-on before anything ships.
• Partner with engineers to bring winners to production faithfully - no gap between what was benchmarked and what ships - and follow through into A/B test readouts.
• Track the fast-moving model and research landscape and re-evaluate as new models, techniques, and papers appear, keeping us on the best quality-per-euro frontier.
• See your work impact the way millions of job seekers find their next job in 42 countries.
• Be surrounded by colleagues who understand you and help you grow in your role.

Qualifications

• Degree in Computer Science, AI, or a related field from a top-tier program.
• Hands-on experience building applied ML or LLM systems - retrieval, RAG, agentic pipelines, or traditional search.
• Genuine data-science depth: design experiments, build baselines, and reason about metrics and their failure modes.
• Fluent in Python and comfortable owning a pipeline end to end: data, prompts, embeddings, retrieval, evaluation, and the glue in between.
• Ability to communicate clearly with non-ML colleagues and make findings understandable.
• Pragmatic and 80/20-minded: prefer shipping a simple approach that captures most of the gain over a complex one.

Requirements

• A track record of taking methods from research papers into working systems or contributions of your own (publications, open source, competition results).
• Experience with latency-sensitive production ML and knowing where in a product expensive calls are acceptable.
• Experience running or analyzing A/B tests on search or recommendation systems.
• Exposure to multiple model providers and cost/latency benchmarking across them.

Benefits

• One of the most exciting applied-AI roles in Germany.
• A rare chance to own the intelligence behind how millions of people find their next job.
• Work with the latest AI tooling - Claude Code and whatever comes next.
• Friendly, kind, and fast-paced work environment.
• Good conversations and moments with experienced colleagues.
• Memorable team experiences, from EU meetups to annual company summer getaways.