Senior Machine Learning Engineer

📍 Remote · US or Canada · Full-time

💰 $165K–$225K per year

🧠 Machine Learning · Computer Vision · Generative Modeling · Digital Pathology

🛂 Visa sponsorship not available

About the company

A virtual staining and digital pathology company building AI-powered technology for clinical workflows. The company develops machine learning systems that support high-fidelity virtual staining, large-scale pathology image analysis, and production-ready model deployment for real-world healthcare environments.

About the role

We are seeking an experienced Senior Machine Learning Engineer to own the representation-learning and generative modeling stack powering virtual staining technology.

The ideal candidate has deep expertise in machine learning, computer vision, image processing, generative modeling, and production-ready ML systems. This person should be able to build generalizable models and evaluations that can stand up in clinical workflows.

This is a senior role for someone with 5–10 years of experience as a Machine Learning Engineer or ML Inference Engineer, ideally at a top tech company.

What you’ll do

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 other 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 and identify opportunities to apply novel techniques to the product roadmap.

Collaborate with ML Engineering and software teams to translate research prototypes into production-ready systems that meet 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.

Must-haves

5–10 years of experience as a Machine Learning Engineer or ML Inference Engineer, ideally at a top tech company.

Experience with image processing, ideally for large, high-resolution images such as pathology, drone imagery, microscopy, satellite imagery, or similar large-format visual data.

Experience with inference processing and model hosting.

Undergraduate degree in Computer Science from a top 100 school.

PhD preferred or Master’s degree in Computer Science, Electrical Engineering, or a related field.

Deep expertise in computer vision and deep learning.

Hands-on experience with one or more of the following: Vision Transformers, Diffusion Models, GANs, semantic segmentation, classical image enhancement, or denoising.

Expert proficiency in Python, PyTorch, and TensorRT.

Experience with AWS for hosting and scaling models.

Strong mathematical foundation in linear algebra, probability, and optimization.

Experience with large-scale model training, distributed computing, or cloud ML infrastructure.

Knowledge of handling large-scale image data, data version control, model registry, and ML lifecycle management.

Experience with feature search, data balancing, and data curation pipelines.

Knowledge of software engineering best practices, including Git and CI/CD pipelines.

Excellent collaboration and communication skills.

Ability to learn quickly in a rapidly changing field.

Ability to work effectively in a fast-paced, cross-functional, international startup environment.

Extensive use of AI tools for coding, optimization, and ideation.

Nice-to-haves

Experience with medical imaging, digital pathology, or whole slide image processing.

Experience working with large, high-resolution image data such as pathology, WSI, microscopy, satellite imagery, drone imagery, or other gigapixel-scale datasets.

Experience with LoRAs, transformer architecture, and state-of-the-art image-to-image translation models such as Flux 2 and Z-Image.

Experience with the Hugging Face ecosystem.

Background in generative models and fine-tuning foundation models.

Experience with GPU acceleration and optimization, including CUDA kernel engineering, TensorRT/ONNX export, and inference serving frameworks such as Triton.

Experience hosting computer vision model inference on NVIDIA DGX Spark.

Understanding of FDA regulatory requirements for AI/ML in medical devices.

Experience with MLOps tools such as MLflow and Kubeflow.

Experience developing tools and frameworks to streamline ML research workflows, experimentation, and reproducibility.

Who thrives here

Someone who learns quickly in a rapidly changing technical field.

Someone deeply technical, research-oriented, and execution-driven.

Someone who can translate state-of-the-art ML research into production-ready systems.

Someone comfortable working across ML Engineering, software engineering, data science, and pathology domain experts.

Someone excited to work on AI technology with real clinical impact.

Traits to avoid

AI-generated resumes.

Candidates with multiple short stints at previous companies.

Job skills

Machine Learning, ML Inference, Computer Vision, Deep Learning, Image Processing, High-Resolution Image Processing, Digital Pathology, Whole Slide Imaging, Medical Imaging, Histopathology Imaging, Virtual Staining, Image-to-Image Translation, Generative Modeling, Representation Learning, Vision Transformers, Diffusion Models, GANs, Semantic Segmentation, Python, PyTorch, TensorRT, AWS, Model Hosting, Cloud ML Infrastructure, Distributed Computing, Large-Scale Model Training, Data Curation, Data Version Control, Model Registry, ML Lifecycle Management, Git, CI/CD, CUDA, ONNX, Triton, MLOps, MLflow, Kubeflow, Hugging Face, Linear Algebra, Probability, Optimization, FDA AI/ML Medical Devices, AI Coding Tools, Research Workflows, Experimentation, Reproducibility, Cross-functional Collaboration, Communication, Fast Learning, and Startup Environments.