Lead ML Engineer - Computer Vision

Benchmark Construction Technology Corp; India; On-site; INR 1,500,000 - 2,500,000; Full time

Benefits offered by this job

Competitive salary + meaningful equity

Comprehensive health, dental, vision benefits

Professional development budget

Job summary

A cutting-edge technology firm in India is seeking a Senior Machine Learning Engineer to architect and deploy ML systems for document analysis. The role demands a strong background in computer vision, Python expertise, and hands-on experience with frameworks like PyTorch. Responsibilities include leading R&D efforts, mentoring junior engineers, and ensuring high-quality code. Competitive salary and benefits are offered along with significant equity options.

Qualifications

• 8+ years of experience in Machine Learning Engineering, with deep focus in computer vision.
• Expert Python skills, with strong experience in PyTorch and FastAPI.
• Comfortable in Linux; hands-on with Docker/containerization for deployment.

Responsibilities

• Architect, build, and deploy ML systems for analyzing building plans.
• Drive R&D across computer vision and NLP as applied to construction documents.
• Mentor junior engineers and ensure code quality.

Skills

Machine Learning Engineering

Computer Vision

Python

PyTorch

FastAPI

Docker

MLops Infrastructure

Image Processing

Job description

Benchmark (formerly BotBuilt, Y Combinator W21) is building the next generation of technology behind American homebuilding. We’re starting with one of the hardest parts of the stack: reliably turning messy, real-world construction plans into structured, usable data.

About The Role

You’ll own and expand our AI-enabled plan ingestion pipeline—from raw PDFs to clean, dependable outputs that power takeoff, design, and downstream automation.

What You’ll Do

• Architect, build, and deploy ML systems for object detection, segmentation, document understanding, and information extraction from complex building plans
• Drive R&D across computer vision and/or NLP as applied to construction documents and related data
• Develop multi-modal approaches that combine vision with language features for document analysis (LLMs/NLP). Familiarity with different RAG methods is a plus
• Mentor junior engineers and help raise the bar on execution, code quality, and decision‑making
• Ensure systems are reliable, maintainable, testable, and observable in production

Qualifications

• 8+ years of experience in Machine Learning Engineering, with deep focus in computer vision
• Expert Python skills, with strong experience in PyTorch and FastAPI
• Proven work with segmentation models, detection frameworks, and OCR/document pipelines
• Strong image processing fundamentals (OpenCV, geometric transforms, preprocessing, etc.)
• Comfortable in Linux; hands‑on with Docker/containerization for deployment
• Experience building and maintaining MLops infrastructure (e.g., ClearML, MLflow)
• Ability to design scalable, high‑performance ML systems (not just prototypes)
• Strong problem‑solving ability and comfort operating independently in a fast‑paced environment

What Makes You a Great Fit

• High agency: you ship, iterate, and fix what’s broken—without waiting for permission
• You’ve delivered ML systems end‑to‑end in production (data → model → deployment → iteration)
• You’re a strong engineer who cares about getting better—trajectory matters here
• You communicate clearly, especially with a remote and cross‑cultural team
• You’re gritty and genuinely enjoy solving hard problems

Bonus Points

• Experience building systems for document reading / understanding
• Ambitious, driven, and opinionated about quality and craft
• Experience with deep learning inference frameworks (e.g., TensorRT, PyTorch compile) and optimizing inference performance
• You keep up with current ML/AI developments and can apply them pragmatically

What We Offer

• Competitive salary + meaningful equity
• Comprehensive benefits (health, dental, vision)
• Professional development budget (courses, conferences, research exploration)
• Real‑world ML problems with direct impact
• High growth potential