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Role overview
Job description
Research Engineer, Applied AI Location: Bangalore (or throughout India remote-friendly with travel) 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 workloads—from large language models to diffusion-based generators to multimodal systems—represent some of the most compute-intensive frontiers in AI, and some of the most promising applications for our
…About this listing
- Source: EnCharge AI's official careers site (via Greenhouse) · last confirmed on the source 3 Nov 2026
- Posted 26 Jun 2026
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What they are looking for
Skills & requirements
Qualification
5+ 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 papers Nice to Have: Experience with efficient inference techniques (KV cache optimization, attention variants, MoE routing, flow matching) Background in hardware-aware ML optimization or quantization Familiarity with profiling tools (PyTorch Profiler, Nsight, custom instrumentation) Publications in generative modeling, efficient inference, or ML systems Contributions to open-source ML projects
Department · Applied Research
Hiring company
EnCharge AI
AI Compute from the Edge-to-Cloud for Every Business. Transformative technology for AI computation, breaking records in efficiency and sustainability to enable state-of-the-art models uninhibited by power, space, and cost constraints.
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