The Biological Computing Co. develops neuron-based computing systems that integrate living neurons with AI models to deliver more efficient, low-power alternatives to silicon hardware.
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