Company Overview

Simplismart enables businesses to build a scalable production-grade AI system and manage the development lifecycle without writing a single line of code. This helps them ship deep learning models in days instead of months, saving them thousands in engineering costs. Our platform allows users to train and monitor ML models collaboratively on any data or use-case. Headquartered in Bengaluru, we belong to the Technology, Information, and Internet industry.

Job Overview

We are looking for a Mid-Level ML Research Engineer to join our team in Bangalore. The ideal candidate should have up to 6 years of work experience and a deep understanding of generative AI, CUDA, and research. This is a full-time position where you will be part of a vibrant team focused on innovative machine learning solutions.

Qualifications and Skills

• Strong expertise in Generative AI (Mandatory skill)
• Proficiency with CUDA for GPU programming and performance optimization (Mandatory skill)
• Prior experience in conducting and publishing research (Mandatory skill)
• In-depth knowledge of various machine learning algorithms and their applications
• Experience with large language models (LLM), such as GPT-3 and beyond
• Skills in working with whisper and Stable Diffusion techniques
• Proficiency in deploying and managing machine learning models using Kubernetes
• Strong analytical and problem-solving abilities

Roles and Responsibilities

• Design, develop, and deploy machine learning models tailored to various business needs
• Conduct cutting-edge research to enhance our machine learning algorithms and systems
• Collaborate with cross-functional teams to integrate ML models into the platform
• Optimize models for performance and scalability using tools like CUDA
• Stay up-to-date with the latest advancements in machine learning and AI technology
• Implement and manage Kubernetes clusters for efficient model deployment
• Provide mentorship to junior engineers and contribute to a collaborative work environment
• Prepare technical documentation and research papers for internal and external dissemination