Note: The job is a remote job and is open to candidates in USA. The company is a virtual staining company using AI-powered technology to advance digital pathology and clinical diagnostics. The Senior Machine Learning Engineer will own the representation-learning and generative modeling stack for virtual staining, designing and evaluating production-ready computer vision and deep learning models. The role also involves collaborating across engineering, data science, and pathology teams to integrate research into scalable production systems.
Responsibilities
- Design and implement novel computer vision and deep learning algorithms for virtual staining and digital pathology applications
- Conduct rigorous experiments to evaluate algorithm performance, validate research hypotheses, and drive iterative improvements
- Develop and advance ML models leveraging Vision Transformers, Diffusion Models, GANs, and generative architectures for image-to-image translation tasks
- Apply classical and learned image enhancement, denoising, and semantic segmentation techniques to histopathology imaging challenges
- Explore image representation in latent space for efficient, high-fidelity virtual staining
- Stay current with state-of-the-art research, identifying opportunities to apply novel techniques to PictorLabs’ product roadmap
- Collaborate with ML Engineering and software teams to translate research prototypes into production-ready systems meeting latency and throughput requirements
- Work with large-scale pathology datasets to train, validate, and fine-tune foundation models and custom architectures
- Partner with software engineers, data scientists, and pathology domain experts to integrate research into production systems
- Contribute to best practices for data engineering, data governance, and data quality across research and production pipelines
- Leverage AI coding and ideation tools to accelerate research velocity and prototype new approaches
Skills
- PhD (preferred) or Master's degree in Computer Science, Electrical Engineering, or a related field
- Deep expertise in computer vision and deep learning, with hands-on experience in one or more of: Vision Transformers, Diffusion Models, GANs, semantic segmentation, or classical image enhancement and denoising
- Expert proficiency in Python and PyTorch and other scientific computing environments a plus
- Strong mathematical foundation in linear algebra, probability, and optimization
- Experience with large-scale model training, distributed computing, or cloud ML infrastructure (AWS, GCP, or Azure)
- Knowledge of handling large scale image data, data version controls, model registry, has experience dealing with ML lifecycles
- Experience with feature search, data balancing, and data curation pipelines
- Knowledge of software engineering best practices including version control (Git) and CI/CD pipelines
- Excellent collaboration and communication skills, with the ability to work effectively in a fast-paced, cross-functional international startup environment
- Extensive use of AI tools for coding, optimization, and ideation
- Experience with medical imaging, digital pathology, or whole slide image (WSI) processing
- Experience with LoRAs, transformer architecture and state of the art image to image translation models (Flux 2, Z-Image) and the Hugging face ecosystem
- Background in generative models and fine-tuning of foundation models
- Experience with GPU acceleration and optimization, including CUDA kernel engineering, TensorRT/ONNX export, and inference serving frameworks such as Triton
- Experience with hosting computer vision model inference on NVIDIA DGX Spark
- Understanding of FDA regulatory requirements for AI/ML in medical devices
- Experience with MLOps tools (MLflow, Kubeflow) and model versioning practices
- Develop tools and frameworks to streamline ML research workflows, experimentation, and reproducibility
Benefits
- Fully remote role for US/Canada based candidates
- Equity
Company Overview