Senior AI Scientist -Research Engineer, Applied AI ( at par with Director)

EnCharge AI, Inc.; India; On-site; INR 6,000,000 - 9,000,000; Full time

Job summary

EnCharge AI, Inc. seeks a Principal Research Engineer in Applied AI to advance AI model capability, quality, and efficiency. You will design fine-tuning pipelines, create rigorous benchmarking, and bridge ML research with hardware-aware optimization for our in-memory AI accelerator.

You will read papers, implement cutting-edge techniques, and deliver production-grade code to accelerate inference, reduce cost, and improve performance across models and deployments.

Qualifications

• 15-20 years of experience in ML research, applied ML, or ML systems
• Strong fundamentals in Python and PyTorch
• Hands-on experience with transformers, diffusion models, state space models etc.
• Experience fine-tuning large models and building training/evaluation pipelines
• Deep understanding of transformers, attention mechanisms, & optimization techniques

Responsibilities

• Algorithmic Acceleration: Research and implement state-of-the-art techniques to accelerate AI inference—quantization, sparsity, distillation, speculative decoding, caching strategies, and architectural modifications.
• Hardware Co-Design: Partner closely with hardware, compiler, and quantization teams to translate improvements to our silicon.
• Evaluation: Build profiling tools and benchmarking frameworks to measure compute bottlenecks and efficiency.
• Applied Research: Build robust fine-tuning workflows for modern AI models, enabling rapid experimentation with LoRA, adapters, and full fine-tuning.

Skills

Python

PyTorch

Transformers

Fine-tuning

Model inference

Tools

Nsight

PyTorch Profiler

Custom instrumentation

Job description

Principal Research Engineer, Applied AI

Location: Delhi / Bangalore

Principal Research Engineer, Applied AI

About EnCharge AI:

EnCharge AI is building the next generation AI platform. Our novel in-memory-computing architecture delivers a 10x step-function improvement in compute energy efficiency and performance for AI inference workloads. As the demands of artificial intelligence move beyond today's models, we believe fundamental underlying infrastructure must evolve. We are an experienced team of AI researchers, silicon & systems engineers, and architects backed by leading investors, poised to become the essential platform for the next wave of AI innovation.

The Opportunity:

Modern AI workloadsfrom large language models to diffusion-based generators to multimodal systemsrepresent some of the most compute-intensive frontiers in AI, and some of the most promising applications for our hardwares energy efficiency advantages. Were building a vertically integrated AI stack that will showcase the transformative potential of our silicon while delivering real value to customers today.

We are seeking a Research Engineer to push the boundaries of AI model capability, quality, and efficiency. Youll build fine-tuning and post training pipelines, develop rigorous benchmarking frameworks, and work at the intersection of ML research and hardware-aware optimizationensuring our models run beautifully on our silicon.

This is a role for someone who thrives at the boundary between research and engineering. Youll read papers, implement techniques, and ship production-quality codeall in service of making AI inference faster, cheaper, and better.

Key Responsibilities:

• Algorithmic Acceleration: Research and implement state-of-the-art techniques to accelerate AI inferencequantization, sparsity, distillation, speculative decoding, caching strategies, and architectural modifications. Systematically characterize tradeoffs between model quality, latency, throughput, and power consumption to find optimal operating points across different use cases.
• Hardware Co-Design: Partner closely with hardware, compiler, and quantization teams to ensure algorithmic improvements translate to real gains on our silicon. Identify optimizations aligned with our architecture's strengthsmaximizing throughput while minimizing power. Shape the feedback loop between model development and hardware.
• Evaluation: Build profiling tools and comprehensive benchmarking frameworks to understand compute bottlenecks, measure model quality across standard and domain-specific evals, and track efficiency metrics.
• Applied Research: Build robust fine-tuning workflows for modern AI models, enabling rapid experimentation with LoRA, adapters, and full fine-tuning. Stay current with the rapidly evolving landscapeevaluate new architectures, implement promising techniques, and contribute insights that inform technical and go-to-market strategy.

Qualifications:

• 15-20 years of experience in ML research, applied ML, or ML systems
• Strong fundamentals in Python and PyTorch
• Hands-on experience with transformers, diffusion models, state space models etc.
• Experience fine-tuning large models and building training/evaluation pipelines
• Deep understanding of transformers, attention mechanisms, & optimization techniques
• Comfort reading and implementing techniques from research papershire , mentor and retain Top Notch AI professionalsNice to Have:

Nice to Have:

• Experience with efficient inference techniques (KV cache optimization, attention variants, MoE routing, flow matching)Background in hardware-aware ML optimization or quantizationFamiliarity with profiling tools (PyTorch Profiler, Nsight, custom instrumentation)Publications in generative modeling, efficient inference, or ML systemsContributions to open-source ML projects

Skills / education

• Pytorch transformers AI ML DL Algorithms Workloads IIT NIT IIIT iisc models / modeling LoRA Python "AI Accelerators" Inference

Contact:

Uday Mulya Technologies hidden_email "Mining The Knowledge Community" .