Machine Learning Engineer

About N71N71 is an AI intelligence platform built for companies that are tired of asking questions and waiting for answers. We connect a company's data — across CRMs, email, documents, and communication tools — into a unified knowledge graph, and use it to surface the insights that matter before anyone has to ask for them. We call it proactive intelligence, and it changes how teams operate.

We're a small, high-conviction team building something that hasn't been built before. If you want to own a technical problem from first principles and see your work shape the product directly, this is the role.

The Role

The quality of what N71 surfaces to users lives or dies on retrieval. When our system decides what context is relevant to a user, what information to pull, and in what order to present it — that's your work.

You will own the retrieval and ranking layer of N71's intelligence stack. That means building and improving the pipelines that take a user's context or query and return the most relevant, timely, and accurate information from across their company's data. You'll work closely with our knowledge graph infrastructure and the product layer to make the intelligence feel sharp and trustworthy.

This is not a research role. We move fast, we ship, and we care about what works in production.

• What You'll Work OnDesign and build hybrid retrieval pipelines — combining dense vector search, sparse keyword retrieval, and graph traversal — for real-world business data
• Fine-tune and evaluate embedding models for domain-specific enterprise content (emails, CRM records, documents, Slack threads)
• Build ranking and re-ranking systems that account for recency, relevance, source reliability, and user context
• Create evaluation frameworks to measure retrieval quality systematically, so improvements are measurable and regressions are caught early
• Optimize latency and cost across retrieval at scale, without sacrificing quality
• Collaborate with the product team to translate retrieval improvements into user-facing intelligence features

What We're Looking For

• 3+ years of hands-on experience building retrieval systems in production
• RAG pipelines, semantic search, or recommendation systems
• Strong fundamentals in information retrieval: you know why BM25 still matters and when dense retrieval falls short
• Experience with vector databases (Pinecone, Weaviate, Qdrant, or similar) and embedding model evaluation
• Comfort working with graph-structured data is a strong plus
• An engineering mindset: you write clean, testable code and care about reliability as much as performance
• Low ego, high ownership — you're comfortable working in ambiguity and making judgment calls with incomplete information
• Bonus Points
• Experience building retrieval systems over multi-modal or heterogeneous enterprise data
• Familiarity with knowledge graph representations and how they complement vector retrieval
• Prior work at an early-stage startup or in a zero-to-one product environment

What We Offer

• Direct access to founders and real influence over product direction
• A focused, technically ambitious team with no bureaucracy
• Competitive salary benchmarked to MENA market rates, with flexibility for the right candidate
• Equity at an early stage
• We hire people who want to build something that matters 💜