Data Engineer / 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.
WORK WITH USA
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