About the Role We’re looking for a Machine Learning Engineer to own and push the limits of model inference performance at scale. You’ll work at the intersection of research and production—turning cutting-edge models into fast, reliable, and cost-efficient systems that serve real users. This role is ideal for someone who enjoys deep technical work, profiling systems down to the kernel/GPU level, and translating research ideas into production-grade performance gains. What You’ll Do • Optimize inference latency, throughput, and cost for large-scale ML models in production • Profile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO) • Implement and tune techniques such as: • Quantization (fp16, bf16, int8, fp8) • KV-cache optimization & reuse • Speculative decoding, batching, and streaming • Model pruning or architectural simplifications for inference • Collaborate with research engineers to productionize new model architectures • Build and maintain inference-serving systems (e.g. Triton, custom runtimes, or bespoke stacks) • Benchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups • Improve system reliability, observability, and cost efficiency under real workloads What We’re Looking For • Strong experience in ML inference optimization or high-performance ML systems • Solid understanding of deep learning internals (attention, memory layout, compute graphs) • Hands-on experience with PyTorch (or similar) and model deployment • Familiarity with GPU performance tuning (CUDA, ROCm, Triton, or kernel-level optimizations) • Experience scaling inference for real users (not just research benchmarks) • Comfortable working in fast-moving startup environments with ownership and ambiguity Nice to Have • Experience with LLM or long-context model inference • Knowledge of inference frameworks (TensorRT, ONNX Runtime, vLLM, Triton) • Experience optimizing across different hardware vendors • Open-source contributions in ML systems or inference tooling • Background in distributed systems or low-latency services Why Join Us • Real ownership over performance-critical systems • Direct impact on product reliability and unit economics • Close collaboration with research, infra, and product • Competitive compensation + meaningful equity at Series A • A team that cares about engineering quality, not hype