Lead Deep Learning Engineer

WORK MODE: Full-Time | Hybrid - Bangalore | 3 Days WFO

EXPERIENCE: 5+ Years (shipping deep learning models into production)

JOB DESCRIPTION:

The Role

We are seeking a Lead Deep Learning Engineer to lead the development of the companys Visual AI stack.

This is a highly technical Senior Individual Contributor role with strategic ownership. You will work with large-scale, real-world culinary vision data captured from robots operating in homes and convert that data into production-grade perception and autonomy systems.

This role requires model inventors and AI architects, not just pipeline optimizers.

Key Responsibilities

1. Model Architecture & Native AI Development

• Design and train transformer-based computer vision models from first principles
• Develop capabilities across: Segmentation | Object detection | Classification | Regression
• Work extensively with Vision Transformer architectures such as: ViT | Swin Transformer | MViT | SegFormer
• Make principled tradeoffs across latency, reliability, cost, and deployed performance

This role demands hands-on experience building and experimenting with transformer architecturesnot merely fine-tuning pre-trained models or integrating APIs

2. Autonomy & Model Strategy

• Define the appropriate autonomy targets given real-world kitchen variability
• Translate autonomy goals into a clear Operational Design Domain
• Decide when to use large general models vs. distilled or task-specific models
• Make strategic shifts based on real-world constraints and deployment learnings

This is not just execution, you will influence core technical direction.

3. Data Strategy & Failure-Driven Learning

• Define what data should be collected and in what sequence
• Balance on-device data, human demonstrations, and curated datasets
• Design robust feedback loops using: Intervention data | Edge case logging | Replay and prioritization frameworks
• Continuously improve reliability in deployed consumer environments

4. Cross-Functional Leadership

You will collaborate closely with:

• Data & Annotation teams working on culinary workflows
• Core Software teams integrating perception models into autonomous cooking systems

You will lead a highly capable team while remaining deeply hands-on.

Ideal Candidate Profile

Required

• 5+ years of experience shipping deep learning models into production
• Deep, hands-on expertise in Vision Transformers
• Proven experience designing and training models from scratch
• Strong architectural intuition and first-principles thinking
• Demonstrated ownership over technical decisions and model direction

Preferred

• Broad computer vision experience across multiple perception domains
• Experience with real-world, noisy, physical-environment data
• Background in segmentation, detection, and state-change recognition tasks
• Comfort operating in ambiguity and defining strategy

Important Clarification

This is not primarily:

• An ML infrastructure optimization role
• A DeepStream/TensorRT-heavy deployment engineering position
• A YOLO-integration or API-centric ML role
• A RAG/chatbot-focused AI position

We are specifically looking for engineers who design and invent AI models, not those who primarily scale or deploy them.

Why This Opportunity

• Work on real-world autonomy at consumer scale
• Direct impact on AI operating in physical environments
• High ownership and strategic influence
• Join a mission-driven team building category-defining robotics products

About the Company:

A well-funded, Series A consumer robotics company building autonomous kitchen robots that cook complete meals in real homes. Their AI-powered systems are deployed at scale in the U.S. market and operate daily in dynamic, real-world kitchen environments.

This is one of the few teams globally shipping cutting-edge AI into the physical world at consumer scale.