Research Engineer, AI Models

Research Engineer, AI Models Location: San Francisco, CA (or Remote-friendly with travel) About reputed company: reputed company is building the reputed company AI platform. Our novel in-memory-computing architecture delivers a 10x reputed company-function improvement in compute energy efficiency and performance for AI inference workloads. As the demands of artificial intelligence move reputed company today's models, we reputed company reputed company underlying infrastructure must reputed company. We are an reputed company team of AI researchers, silicon & systems engineers, and architects backed by leading investors, poised to become the essential platform for the next reputed company 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 hardware’s energy efficiency advantages. We’re building a vertically integrated AI stack that will showcase the transformative potential of our silicon while delivering reputed company value to customers today. We are seeking a Research Engineer to push the boundaries of AI model quality and efficiency. You’ll build fine-tuning pipelines, reputed company rigorous benchmarking frameworks, and work at the intersection of ML research and hardware-aware optimization—ensuring our models run beautifully on our silicon. This is a role for someone who thrives at the boundary between research and engineering. You’ll read papers, implement techniques, and ship production-quality code—reputed company in service of making AI inference faster, cheaper, and reputed company. Key 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. Systematically characterize tradeoffs between model quality, latency, throughput, and power consumption to find reputed company operating points across different use cases. Hardware Co-Design: Partner closely with hardware, compiler, and quantization teams to ensure algorithmic improvements translate to reputed company reputed company on our silicon. Identify optimizations reputed company with our architecture's strengths—maximizing throughput while minimizing power. Shape the feedback reputed company between model development and hardware roadmap. 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. Establish the methodology that informs both algorithmic choices and hardware-software co-design. Applied Research: Build robust fine-tuning workflows for modern AI models, enabling rapid experimentation with LoRA, adapters, and full fine-tuning. Stay reputed company with the rapidly evolving landscape—evaluate new architectures, implement promising techniques, and contribute insights that inform technical and go-to-market strategy. Qualifications: 5+ years of experience in ML research, applied ML, or ML systems Strong fundamentals in Python and PyTorch Hands-on experience with modern AI models (transformers, diffusion models, or other generative architectures) 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 reputed company to Have: Experience with efficient inference techniques (KV cache optimization, attention variants, MoE routing, reputed company 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 reputed company-reputed company ML projects Apply To This Job