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Senior AI Scientist -Research Engineer, Applied AI ( at par with Director) Job Description: 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-theart 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 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 domain-specific 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: 15-20 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 hire , mentor and retain Top Notch AI professionals 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 open-source ML projects .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.