Sr. Machine Learning Engineer

About Pictor LabsPictor 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 RoleWe 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 USCompany: Pictor LabsEmployment Type: Full-timeResponsibilitiesDesign, development, and optimization of production ML inference systems for virtual staining models (Deepstain, Restain, ClearStain) serving clinical and pharmaceutical customersArchitect and implement high-performance inference pipelines capable of processing gigapixel pathology images with sub-2-minute latency requirementsWork 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 throughputProfile and optimize deep neural networks on NVIDIA GPUs using tools such as NVIDIA Nsight, PyTorch Profiler, and custom instrumentationDesign and implement efficient model serving architectures that support both synchronous REST APIs and asynchronous batch processing workflowsCollaborate with Platform and Edge Device teams to containerize inference systems (Docker, Kubernetes) for deployment across cloud and on-premise environmentsPartner with cloud providers (AWS, GCP, Azure) to optimize hosted inference solutions and leverage latest hardware acceleratorsEnsure inference systems meet regulatory requirements (FDA 510(k), SOC2) with comprehensive monitoring, logging, and audit capabilitiesPrototype and productionize new inference optimization techniques, including quantization, pruning, distillation, and dynamic batching strategiesBuild robust telemetry and monitoring systems to track model performance, latency, throughput, and resource utilization in productionQualificationsRequired:7+ years of experience building and optimizing production ML inference systems at scaleExpert-level proficiency in Python and experience writing high-performance inference services5+ years of hands-on experience with PyTorch and at least one production inference tools (TensorRT, Triton Inference Server, ONNX Runtime, TorchServe)Deep understanding of computer vision model architectures, particularly generative models (GANs, diffusion models) and vision transformersExtensive experience profiling and optimizing deep neural networks on NVIDIA GPUs, including memory optimization, kernel fusion, and mixed-precision inferenceStrong background in image processing pipelines and libraries (OpenCV, Pillow, scikit-image) for handling large-scale medical imaging dataProven experience deploying ML systems on Kubernetes and major cloud providers (AWS, GCP, Azure)Experience with Docker containerization and orchestration for ML workloadsStrong software engineering practices including version control (Git), CI/CD, unit testing, and production debuggingExcellent communication, collaboration, and technical documentation skillsPreferred:Experience with medical imaging, digital pathology, or whole slide imaging (WSI) processingKnowledge of edge device deployment and embedded systems for AI inferenceExperience with MLOps tools (MLflow, Kubeflow, Apache Airflow) and model versioningUnderstanding of FDA regulatory requirements for AI/ML in medical devicesBackground in distributed inference systems and model parallelism techniquesFamiliarity with monitoring and logging tools (Prometheus, Grafana, ELK Stack)What We OfferThe opportunity to work on technology that directly improves patient outcomes and transforms clinical diagnostics, alongside a talented team of engineers and researchers pushing the boundaries of AI in healthcare.PictorLabs is an equal opportunity employer and does not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability, or other legally protected statuses. #J-18808-Ljbffr