Principal Research Engineer (Applied AI)

Location: India (or Remote-friendly with travel)

About EnCharge AI

EnCharge AI is building the next generation AI platform. Our novel inmemorycomputing architecture delivers a 10x stepfunction 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 diffusionbased generators to multimodal systemsrepresent some of the most computeintensive 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 finetuning and posttraining pipelines, develop rigorous benchmarking frameworks, and work at the intersection of ML research and hardwareaware 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 productionquality codeall in service of making AI inference faster, cheaper, and better.

Key Responsibilities

Algorithmic Acceleration: Research and implement stateoftheart 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 CoDesign: Partner closely with hardware, compiler, and quantization teams to ensure algorithmic improvements translate to real gains on our silicon. Identify optimizations aligned with our architectures 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 domainspecific evals, and track efficiency metrics.

Applied Research: Build robust finetuning workflows for modern AI models, enabling rapid experimentation with LoRA, adapters, and full finetuning. Stay current with the rapidly evolving landscapeevaluate new architectures, implement promising techniques, and contribute insights that inform technical and gotomarket strategy.

Qualifications

10+ years of experience in ML research, applied ML, or ML systems

Strong fundamentals in Python and PyTorch

Handson experience with transformers, diffusion models, state space models etc.

Experience finetuning large models and building training/evaluation pipelines

Deep understanding of transformers, attention mechanisms, & optimization techniques

Comfort reading and implementing techniques from research papers

Nice to Have

Experience with efficient inference techniques (KV cache optimization, attention variants, MoE routing, flow matching)

Background in hardwareaware ML optimization or quantization

Familiarity with profiling tools (PyTorch Profiler, Nsight, custom instrumentation)

Publications in generative modeling, efficient inference, or ML systems

Contributions to opensource ML projects Location: India (or Remote-friendly with travel)

About EnCharge AI

EnCharge AI is building the next generation AI platform. Our novel inmemorycomputing architecture delivers a 10x stepfunction 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 diffusionbased generators to multimodal systemsrepresent some of the most computeintensive 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 finetuning and posttraining pipelines, develop rigorous benchmarking frameworks, and work at the intersection of ML research