Research Scientist, Performance Engineering

TBC is building next-generation AI systems at the intersection of biological computing, generative models, and large-scale AI infrastructure. As we scale our world-model and neural-optimizer efforts, we are looking for an optimization-focused Research Scientist / ML Engineer to improve the efficiency, latency, throughput, and deployability of large models.

This role is focused on making frontier models run faster, cheaper, and more reliably — especially LLMs, diffusion models, video generation models, and world-model systems. You will work across inference optimization, training efficiency, model compression, memory management, and GPU-level performance to help turn research systems into scalable, customer-ready products.

• 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

• 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 one or more of:

  • Inference optimization

  • KV caching / attention optimization

  • Triton or CUDA kernel development

  • Quantization, pruning, distillation, or model compression

  • Distributed training / fine-tuning efficiency

  • 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

• Large models run faster, cheaper, and more reliably across TBC’s core workloads

• Inference pipelines show measurable improvements in latency, throughput, memory use, and GPU utilization

• Training and fine‑tuning workflows become more efficient, reproducible, and scalable

• Optimization work translates into clear product and customer value, not just internal benchmarks

• Research prototypes become deployable systems that can support demos, evaluations, and early partner use cases

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

• Prior work optimizing large-scale generative models in production or research settings

• Experience with modern inference/training stacks such as PyTorch, Triton, CUDA, vLLM, TensorRT, Deep Speed, FSDP, Ray, or similar tooling

• Experience working with LLMs, diffusion models, video generation models, or world models