Contextual AI Interview Guide (2026): RAG 2.0, Research and Engineering Tracks

Contextual AI was founded in 2023 by Douwe Kiela and Amanpreet Singh, who met as researchers at Meta AI and later worked together at Hugging Face. Kiela led the Meta team that first published the RAG method and now serves as CEO, with Singh as CTO. The company raised a $20M seed round in 2023 led by Bain Capital Ventures, then an $80M Series A in August 2024 led by Greycroft with participation from NVIDIA’s NVentures. Its product trains the retriever and the language model together as one system rather than bolting a vector database onto a general chatbot, and it counts large enterprises such as HSBC and Qualcomm among its customers.

Process

Contextual AI posts most technical openings under a single “Member of Technical Staff” banner, split across research, platform, backend infrastructure, and full stack, so the loop you get depends on which track you apply to. Expect a recruiter screen, then a technical screen, then an onsite of roughly three to five rounds. The engineering track leans on coding plus one or two rounds of ML system design built around retrieval and serving, while the research track weights a domain conversation and a paper or project walkthrough more heavily. Every track includes a behavioral conversation, often with a founder or team lead given the small team size. Public Glassdoor reports put the full cycle around three to four weeks, with software engineering roles trending toward the longer end of that.

What they actually ask

• Retrieval and embeddings fundamentals: how dense retrieval works, when to add a reranker, and the tradeoffs between BM25, dense, and hybrid search.
• RAG architecture and evaluation: how you would design a retrieval pipeline end to end, and how you would measure whether answers are actually grounded in the source documents.
• Grounding and hallucination: how you detect and reduce ungrounded output, including citation and attribution strategies for enterprise data.
• Fine-tuning and training: when to fine-tune versus prompt, and how you would build an evaluation set for a domain-specific model.
• Systems for serving: designing low-latency inference and vector search at scale, plus standard data structures and algorithms in a live coding round.
• Project or paper walkthrough (research track): expect to defend your design choices and explain what you would do differently now.

Levels and comp (2026)

Contextual AI uses the “Member of Technical Staff” title instead of a public numeric ladder, so seniority is negotiated case by case rather than mapped to a fixed level. As a well-funded, Series A stage company in the Bay Area, base salaries for experienced engineers generally land in the rough range of $180K to $250K, with senior and specialized research hires reaching higher, plus meaningful early-stage equity. Because the company is private and relatively young, the equity is the speculative part: it can be worth a great deal or nothing, and the strike price and preferred terms matter more than the headline grant number. Treat any single figure as approximate; check levels.fyi and recent Blind threads for current data points, and ask the recruiter directly for the band and the equity structure before you anchor on a number.

Prep priorities

Be genuinely fluent in retrieval, because it is the center of the product and the center of the conversation. Know how embeddings, rerankers, and chunking interact, and be ready to reason about RAG evaluation, since grounding and faithfulness metrics are exactly what this company sells. Keep your general coding sharp; the bar for data structures and algorithms is real even in ML-heavy roles, and several candidates report a live coding round regardless of track. For the engineering track, practice designing a serving path for retrieval and inference under latency constraints, and be ready to talk about vector databases, indexing, and data orchestration at scale. For the research track, bring one project you can explain in depth and defend under pointed questions. Read the company’s research blog so you can speak to RAG 2.0 and grounded generation in their own terms, and prepare specific behavioral stories about shipping under ambiguity, since the team is small and early.

Frequently Asked Questions

Is Contextual AI a research lab or a product company? Both. It runs a real research agenda on retrieval and grounded generation, but it ships a commercial enterprise platform, so most roles blend applied ML with production engineering.

Do I need a PhD to get hired? For pure research roles a Master’s or PhD is preferred, but platform, backend, and full-stack roles weight strong systems engineering and shipping experience over academic credentials.

How hard is the coding bar? Real. Even ML and research candidates report a live coding round, so practice standard data structures and algorithms alongside your domain prep rather than assuming the ML conversation carries the whole loop.

How long does the process take? Public reports suggest about three to four weeks from screen to offer, with engineering roles sometimes running closer to a month.