At Lynqr, we believe that search is the ultimate bottleneck for AI. Our mission is to create the next generation of Information Retrieval systems: truly multimodal, transformer-native, and capable of surfacing just the right context across any format.

About the role

We are building a full-stack retrieval research lab that rethinks how search works in the era of large models. Our research spans late interaction methods, multimodal retrievers, and LLM-augmented search, with the goal of pushing retrieval beyond its current limits. As part of our team, your work will directly influence both our product, Omni, and the broader field of information retrieval.

What you'll do

• Lead and contribute to applied research projects in Information Retrieval

• Advance Omni, our core search platform, improving retrieval performance and efficiency

• Design and evaluate novel methods for late interaction and multimodal retrieval

• Investigate LLM-augmented search pipelines, identifying strengths and limitations

• Collaborate across engineering and product teams to align research with impact

• Publish and share results through papers, blog posts, and conferences

• Shape the company's research agenda and influence the roadmap of retrieval research

Research Directions

• Late Interaction: Designing the next generation of fine-grained retrieval models beyond ColBERT

• Omni-Modality: Building retrievers that unify text, image, and beyond

• LLM-Augmented Search: Exploring how LLMs generate, rank, and consume retrieval results

• Evaluation: Developing new evals that reflect real-world, agentic use cases

• Data Work: Improving retrieval signals and dataset quality to boost performance

• Interpretability: Investigating how and why search systems succeed or fail

What we're looking for

• Experience in information retrieval, embeddings, or related fields

• Advanced degree (PhD/Master's) or equivalent experience with impactful research projects

• Proficiency in Python and ability to implement models from scratch

• Passion for advancing search beyond single-vector similarity methods

• Broad understanding of the ML lifecycle: algorithms, training, data, efficiency

• Strong written and verbal communication skills

Nice to have

• Publications in top-tier conferences (NeurIPS, ICML, SIGIR, ACL, etc.)

• Open-source contributions in IR, NLP, or ML frameworks

• Familiarity with late interaction models, transformers, or vector search systems

• Experience designing and running large-scale training experiments

What We Offer

• Competitive compensation + equity

• Comprehensive health, dental, and vision coverage

• Visa sponsorship + relocation support

• Professional development budget

• Access to the best AI tools and subscriptions

• Team off-sites + conference attendance

• Transportation support

• Wellness support, including gym membership and sports club subscriptions

• Food support