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