Senior Machine Learning Engineer - Computer Vision & Generative AI (Digital Pathology)
Remote
AI Summary
Design and implement novel computer vision and deep learning algorithms for virtual staining and digital pathology applications. Develop and advance ML models leveraging Vision Transformers, Diffusion Models, GANs, and generative architectures for image-to-image translation tasks. Collaborate with cross-functional teams to translate research prototypes into production-ready systems meeting clinical workflow requirements.
Key Highlights
Own the representation-learning and generative modeling stack for virtual staining
Work with large-scale pathology datasets to train, validate, and fine-tune foundation models
Fully remote role for US/Canada based candidates with salary range 165-225K USD plus benefits and equity
Key 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
Technical Skills Required
Benefits & Perks
Salary range 165-225K USD yearly
Benefits
Equity
Nice to Have
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
Job Description
Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus equity.
We are 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 Engineer to join our team who owns the representation-learning and generative modeling stack that powers Pictor’s virtual staining. The ideal candidate will have deep expertise in Machine Learning and building generalizable, production-ready models, and evaluations that stand up in clinical workflows.
● 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
Collaboration
● 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
Required Qualifications
● 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
Preferred Qualifications
● 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
What We Offer
The 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. You will have the freedom to pursue high-impact research while seeing your work deployed at scale in real clinical environments.
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