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.

WHAT YOU’LL WORK ON

• 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

WHAT WE’RE LOOKING FOR

• 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

WHAT SUCCESS LOOKS LIKE

• 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

PREFERRED QUALIFICATIONS

• 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, DeepSpeed, FSDP, Ray, or similar tooling
• Experience working with LLMs, diffusion models, video generation models, or world models