Sr. Machine Learning Engineer

About Pictor Labs Pictor Labs is the leading virtual staining company revolutionizing digital pathology adoption worldwide through cutting-edge AI-powered technology. Our solutions deliver diagnostic-quality results in minutes while preserving tissue samples for comprehensive analysis. Our breakthrough DeepStain™ and ReStain™ technologies enable unlimited virtual staining from a single tissue sample, eliminating the bottlenecks and limitations of traditional chemical staining processes. This innovation supports the critical evolution from research applications to clinical deployment, empowering laboratories to advance their digital pathology capabilities while reducing chemical waste, improving operational efficiency, and expanding diagnostic possibilities. About the Role We are seeking an experienced Senior ML Inference Engineer to join our team, focusing on optimizing and deploying our production virtual staining models at scale. The ideal candidate will have deep expertise in ML inference optimization, GPU programming, and building production-grade inference systems. You will work on critical challenges such as reducing inference latency for whole slide imaging (WSI) from tens of minutes to under 2 minutes, deploying models on edge devices with NVIDIA hardware, and ensuring our inference infrastructure meets FDA and SOC2 compliance requirements. This role offers the opportunity to work at the intersection of cutting-edge AI and life-saving healthcare technology, making a tangible impact on patient outcomes. Location: Remote US Company: Pictor Labs Employment Type: Full-time Responsibilities Design, development, and optimization of production ML inference systems for virtual staining models (Deepstain, Restain, ClearStain) serving clinical and pharmaceutical customers Architect and implement high-performance inference pipelines capable of processing gigapixel pathology images with sub-2-minute latency requirements Work with ML Research and Engineering teams to optimize model architectures and deployment strategies for both cloud-based APIs and edge devices (NVIDIA DGX Sparc, Grace Blackwell superchips) Evaluate, implement, and maintain state-of-the-art inference frameworks (TensorRT, Triton Inference Server, ONNX Runtime) to maximize GPU utilization and throughput Profile and optimize deep neural networks on NVIDIA GPUs using tools such as NVIDIA Nsight, PyTorch Profiler, and custom instrumentation Design and implement efficient model serving architectures that support both synchronous REST APIs and asynchronous batch processing workflows Collaborate with Platform and Edge Device teams to containerize inference systems (Docker, Kubernetes) for deployment across cloud and on-premise environments Partner with cloud providers (AWS, GCP, Azure) to optimize hosted inference solutions and leverage latest hardware accelerators Ensure inference systems meet regulatory requirements (FDA 510(k), SOC2) with comprehensive monitoring, logging, and audit capabilities Prototype and productionize new inference optimization techniques, including quantization, pruning, distillation, and dynamic batching strategies Build robust telemetry and monitoring systems to track model performance, latency, throughput, and resource utilization in production