Ai engineer

JoVE is the world-leading video platform for scientific research and education. Millions of scientists, educators and students worldwide use JoVE for their research, teaching and learning. Our institutional clients comprise over 1,000 universities, colleges, and biopharma companies, including leaders such as Harvard, MIT, and Stanford. As a rapidly growing company, with offices in the USA, UK, Australia, and India servicing clients in over 60 countries, we are seeking talented and ambitious individuals to join our company.

The Role

As an AI Engineer at JoVE, you will build the retrieval, matching and LLM systems behind our AI research products. You will own features end to end, from prototype and evaluation to production, monitoring and cost. You will work closely with product, the JoVE science editors, and the web and data engineers.

Responsibilities

• Retrieval and search: Build hybrid retrieval (BM25, sparse and dense embeddings) with reranking over tens of millions of papers. Sources include PubMed, OpenAlex and PMC open-access full text.
• Paper-to-video matching: Improve how papers and protocol steps are matched to JoVE videos and timestamps. Make matching aware of organism, technique and study type.
• AI answers with citations: Build RAG and agentic pipelines for cited synthesis, Methods Q&A and troubleshooting. Every claim must trace to a source.
• Protocol understanding: Extract method steps, reagents and parameters from papers. Link each step to the matching video chapter.
• Evaluation: Build eval sets with scientific editors. Track relevance, citation accuracy, hallucination rate and match quality, and gate releases on them.
• Production and cost: Ship and run models and LLM services in production. Watch latency and cost per query, and choose between hosted and open models.
• Platform and APIs: Help build the APIs and an MCP server that let AI assistants, ELNs and publisher widgets use JoVE's methods data.
• Instrumentation: Work with product to measure the search, video click and watch funnel, and use it to improve ranking.

4+ years in software or ML engineering, including 2+ years shipping LLM, search or NLP features to production.

Strong Python and solid software engineering habits: testing, code review, CI/CD.

Hands-on experience building RAG systems: chunking, embeddings, hybrid retrieval, reranking and prompt design.

Experience with a vector or search engine such as Elasticsearch/OpenSearch, Milvus, pgvector, Qdrant or Pinecone.

Experience with LLM APIs (Anthropic, OpenAI or similar) and open models (Hugging Face, vLLM).

A track record of building offline evals and using metrics, not demos, to decide what ships.

Experience with agent frameworks, tool use, or building MCP servers.

Comfort with cloud infrastructure (AWS), Docker and API design.

Clear communication with non-engineers, including scientists and editors.

Nice to have

• Experience with scholarly data: PubMed, OpenAlex, Semantic Scholar, PMC, or entity extraction for genes, organisms and methods.
• Multimodal work: video transcripts, timestamp alignment, or image and video embeddings.
• Fine-tuning embedding or reranker models, or learning-to-rank.
• Experience with large-scale indexing (100M+ documents) and vector quantisation.
• A competitive compensation package.
• We are fully remote!
• You will make a direct impact in accelerating science research and education.
• Opportunity to work with global teams and in an environment that promotes innovation and collaboration.
• Our strong promotion from within culture draws a clear path to advance your career with us.