Member of Technical Staff - AI/ML Engineer

Role

Role

Engineering, Machine learning

Experience

6+ years

Visa

Visa

US citizenship/visa not required

Skills

Skills

Kubernetes, Python, Torch/PyTorch, Machine Learning, Computer Vision, LLMs, AI Agents

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About the role
Job Overview

Were 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 dont 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.

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

What Youll 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 Were 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 thats a gradient-boosted tree, sometimes its 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 types 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 youve 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 familys property purchase is safe.

• Small, senior team with high owners…