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About the Role

TBC is building next-generation AI systems at the intersection of biological computing, generative models, and large-scale AI infrastructure. This role focuses on improving the efficiency, latency, throughput, and deployability of large models, particularly LLMs, diffusion models, video generation models, and world-model systems, to turn research systems into scalable, customer-ready products.

Responsibilities

• Optimize inference for LLMs, diffusion models, video models, and world-model systems
• Improve serving efficiency through techniques such as KV caching, batching, quantization, distillation, speculative decoding, and memory optimization
• Build and optimize high-throughput inference pipelines for large models running on GPU clusters
• Profile model performance across latency, throughput, memory usage, GPU utilization, and cost
• Implement custom kernels or low-level optimizations using Triton, CUDA, PyTorch, or related systems
• Improve training and fine-tuning efficiency for large generative models, including distributed training, checkpointing, parallelism, and data loading
• Work with research teams to identify bottlenecks in model architecture, inference paths, and deployment workflows
• Translate model performance improvements into clear customer-facing benchmarks and technical proof points
• Evaluate trade-offs across model quality, latency, cost, memory, and deployability

Requirements

• Strong background in machine learning systems, model optimization, or high-performance AI infrastructure
• Hands-on experience optimizing LLMs, diffusion models, video generation models, or other large generative systems
• Experience with inference optimization
• Experience with KV caching / attention optimization
• Experience with Triton or CUDA kernel development
• Experience with quantization, pruning, distillation, or model compression
• Experience with distributed training / fine-tuning efficiency
• Experience with GPU profiling and performance debugging
• Strong PyTorch experience and comfort working close to the model/runtime boundary
• Ability to reason about trade-offs between quality, latency, throughput, memory, and cost
• Comfortable working across research code, production systems, and benchmarking infrastructure
• Excited to work in an ambiguous, early-stage environment where optimization work directly shapes product feasibility

Skills

• PyTorch
• Triton
• CUDA

Education Level

• PhD, MS, or equivalent industry experience in Computer Science, Machine Learning, Systems, Robotics, or related field

About the Company

• TBC is building next-generation AI systems at the intersection of biological computing, generative models, and large-scale AI infrastructure.