Member of Technical Staff - AI/ML Engineer

Job Overview

We're looking for an AI engineer with genuine depth across three areas that most people only have one of: classical ML, LLMs, and computer vision. Our problems don't fit neatly into one bucket. A single workflow might involve a vision-language model extracting fields from a 40-year-old scanned sale deed, a ranking model deciding which retrieved records matter, and an LLM-powered agent reasoning over the results to flag title risks.

You'll work on systems that are already in production and used by real customers making high-stakes property decisions - not research prototypes.

What You'll Work On

• Document understanding at scale. Fine-tuning VLMs (Qwen, Nemotron, Kimi family models using LoRA adapters) for classification, layout analysis, and field extraction across Indian property documents - handwritten, scanned, stamped, multilingual, and frequently degraded.

• Classical ML where it earns its keep. Ranking and retrieval (BM25 and learned rankers), entity resolution across noisy government records, fraud/anomaly detection, and calibration of model confidence for legal-grade outputs.

• LLM agents in production. Improving our conversational agents for land-records search and title diligence - tool design, context management, evaluation harnesses, and cost/latency optimization.

• Evaluation and data infrastructure. Designing annotation taxonomies, building eval sets that reflect real document distributions, and closing the loop from production failures back into training data.

What We're Looking For

• 6-10 years building ML systems in production, with shipped work across at least two of: classical ML, LLMs/NLP, computer vision.
• Strong fundamentals - you can reason about why a model fails, not just swap in a bigger one. Comfort with the full lifecycle: data, training, evaluation, deployment, monitoring.
• Hands-on experience fine-tuning open-weight models (LoRA/QLoRA, SFT, preference optimization) or training CV/document models (detection, layout, OCR pipelines).
• Practical LLM engineering: prompt and context design, structured/constrained outputs, RAG, agent tool design, building evals that actually predict production quality.
• Solid Python and the engineering discipline to write code teammates can build on. Experience with PyTorch and the modern inference stack (vLLM or similar) is a plus.
• Pragmatism. You pick the simplest approach that solves the problem - sometimes that's a gradient-boosted tree, sometimes it's an 8B VLM with constrained decoding.

Nice to Have

• Experience with Indic languages, OCR for degraded documents, or multilingual NLP.
• Work on agentic systems, multi-step tool use, or LLM orchestration frameworks.
• Exposure to legal, fintech, or other high-stakes domains where correctness and provenance matter.
• Contributions to open-source ML tooling or published applied work.

Your First 90 Days

Days 1-30: Ground truth. Ship a small improvement to a production model or eval in week one. Read real documents and real transcripts - sale deeds, ECs, agent conversations - until you understand why this data breaks naive approaches. Own one document type's extraction quality end to end.

Days 31-60: Own a model surface. Take full ownership of one pipeline - say, an extraction adapter for a major state or the retrieval/ranking layer - including its eval set, error analysis, and a measurable quality lift you've shipped to production.

Days 61-90: Shape the roadmap. Propose and begin executing a meaningful bet - a new adapter architecture, a better eval harness, a classical-ML component that cuts cost or error - backed by evidence from your first 60 days. By now your judgment should be influencing what the team builds next, not just how.

Why This Role

• Frontier applied-AI problems with no playbook - nobody has solved document intelligence for Indian land records.
• Direct impact: your models decide whether a family's property purchase is safe.
• Small, senior team with high ownership; you'll shape architecture, not just implement tickets.
• Backed by Y Combinator and top investors, with real revenue and real customers.

Why Join Landeed?

• High-Impact Role: Shape the AI backbone of a cutting-edge real estate platform that transforms how millions access property information.

• Fast-Growing Startup: Join a dynamic, collaborative environment in Hyderabad, where your ideas and expertise will be valued.

• Competitive Compensation: Receive a fixed salary plus equity, aligning your success with the company’s growth.

• Professional Growth: Work with talented peers and stay at the frontier of AI/ML innovations in NLP and information retrieval.

We move fast - the full loop takes 5-7 days, and we'll give you a decision within 48 hours of your final round.

• Intro call (30 min). Mutual fit, your background, what you're looking for. We'll walk you through the problem space.

• Technical deep-dive (60 min). A past ML system you shipped, interrogated properly: data, modeling choices, failure modes, what you'd redo. We're probing for depth and honesty, not buzzwords.

• Work sample (take-home or paired, your choice, ~3 hrs). A scoped problem on real (anonymized) property-document data - e.g., design and partially implement an extraction + eval approach.

• System design + eval round (60 min). Design a production pipeline for a Landeed problem end to end, including how you'd measure it. Expect pushback.

• Founder conversation (45 min). Values, ambition, and your questions about where the company is going.