Senior ML Engineer – Computer Vision

Job Title: Lead Applied Researcher / Senior ML Engineer – Computer Vision

Location : Bangalore

Work Mode: Remote

Shift - 2:00 PM - 11:00 PM

JD:

About the Role:

We are seeking an elite Lead Applied Researcher / Senior Machine Learning Engineer to serve as a core technical pillar for our Computer Vision team. In this role, you will take significant ownership of the end-to-end model lifecycle, bridging the gap between state-of-the-art vision research and enterprise-scale production deployments.

Unlike purely full-stack or MLOps integrations, this role requires foundational expertise in core Model Development and Applied Research. The ideal candidate will have deep expertise in detection, tracking, pose estimation, or related vision problems, with the ability to evaluate and adapt modern architectures such as transformer-based detectors and vision foundation models. A key part of the role would be designing highly reproducible, algorithmic data curation and active learning pipelines to systematically conquer complex visual challenges like occlusion, extreme density, and hand-item interaction patterns etc.

Key Responsibilities:

1. Transformative R&D & Advanced Model Architecture

• Next-Gen Vision Paradigms: Comfortable exploring and helping transition to state-of-the-art models, pushing the boundaries beyond standard YOLO/Ultralytics deployments by researching and implementing emerging transformer-based architectures (e.g., RT-DETR, Swin) or self-supervised foundation models (e.g., DINO).
• Targeted Algorithmic Experimentation: Conceptualize high-impact architectural pivots to drive massive leaps in production accuracy, solving for varied environments (e.g., Bin cameras, RFID zones).
• End-to-End Lifecycle Ownership: Architect agile, highly optimized distributed training recipes that reduce model training time from days to hours, ensuring rapid, incremental production rollouts.

2. Algorithmic Data Strategy & Pipeline Engineering

• Idempotent Data Versioning: Architect and scale lineage-tracked pipelines to support dozens of production datasets, enabling the team to deploy new datasets via config files with guaranteed exact reproducibility.
• Multi-Dimensional Data Curation & Sampling: Design scalable sampling strategies that maintain representative coverage across multiple attributes like shopping scenarios, camera, regions etc. Apply multi-attribute stratification, balanced sampling, hard-example mining, and long-tail sampling where appropriate.
• Proactive Failure-Mode Targeting: Utilize active learning frameworks to analysis the model failure and finding out failure pattern of target the failure scenario data collection to help model improve in optimal way

3. Technical Leadership & Cross-Functional Mentorship

• Architectural Translation: Excel at distilling complex CV observations into actionable engineering stories, acting as the bridge between pure research and scalable platform engineering.
• Technical Excellence: Fundamentally raise the team’s technical baseline by actively mentoring data science peers, engineering counterparts, and external vendor partners.

Basic Qualifications:

• Education: Master’s or Ph.D. in Computer Science, Machine Learning, Computer Vision, or a highly related quantitative field.
• Experience: 8–10+ years of industry experience deploying machine learning, computer vision, and deep learning systems at enterprise scale.
• Core CV Expertise: Strong experience in one or more of object detection, multi-object tracking, pose estimation, segmentation, representation learning, or vision transformers.
• Technical Depth: Demonstrated experience designing experiments and improving models through architecture, training strategy, loss/optimization, data curation, and failure analysis—not solely integrating high-level model APIs.
• Tech Stack: Strong Python and PyTorch experience; familiarity with OpenCV and modern ML experimentation/data tooling such as MLflow, Spark, Kubernetes, BigQuery, and cloud-based training infrastructure.

If interested, share your resume at radhika.nalawade@leanitcorp.com