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Diffusion Process Engineer Jobs in New York (NOW HIRING)

Senior Machine Learning Engineer

New York, NY ยท Remote

$165K - $225K/yr

... chemical staining processes. This innovation supports the critical evolution from research ... Vision Transformers, Diffusion Models, GANs, semantic segmentation, or classical image enhancement ...

... Diffusion, and Runway ML. * Prototype interactive experiences, brand moments, or UI/UX flows using ... Document your process and learnings to help build scalable workflows and design systems infused ...

... Diffusion, and Runway ML. * Prototype interactive experiences, brand moments, or UI/UX flows using ... Document your process and learnings to help build scalable workflows and design systems infused ...

Founding iOS Engineer

New York, NY ยท On-site

$200K - $300K/yr

To do this we're developing cutting-edge diffusion models and designing novel, personalized ... Define mobile development processes - including planning, testing, releasing, and monitoring

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Diffusion Process Engineer information

What are the key skills and qualifications needed to thrive in the Diffusion Process Engineer position, and why are they important?

To excel as a Diffusion Process Engineer, you need a strong foundation in materials science, chemical engineering, and semiconductor fabrication processes, generally supported by a relevant engineering degree. Familiarity with cleanroom protocols, process simulation tools (such as TCAD), statistical process control (SPC), and certifications like Six Sigma are commonly expected. Excellent problem-solving skills, attention to detail, and effective communication help you collaborate with cross-functional teams and troubleshoot process issues efficiently. These combined qualifications and skills are essential to ensure high-yield manufacturing processes and continual improvement in semiconductor production environments.

What are common daily responsibilities for a Diffusion Process Engineer in the semiconductor industry?

As a Diffusion Process Engineer, your typical day often includes monitoring and optimizing diffusion furnace operations, analyzing process data, and troubleshooting equipment or yield issues to ensure efficient semiconductor wafer production. You'll work closely with operators, equipment technicians, and other engineers to implement process improvements and maintain strict cleanroom standards. Regular tasks may also involve conducting experiments, documenting procedures, and collaborating on cross-functional teams to support new product introductions or resolve production challenges. This varied workflow provides valuable opportunities to deepen your expertise and contribute to advancing manufacturing technology.

What is a Diffusion Process Engineer job?

A Diffusion Process Engineer is responsible for developing, monitoring, and optimizing diffusion and thermal processes in semiconductor manufacturing. They work with high-temperature furnaces and doping techniques to alter the electrical properties of silicon wafers. Their role includes process development, equipment maintenance, troubleshooting, and ensuring production meets quality and efficiency standards. They collaborate with equipment engineers, integration teams, and manufacturing personnel to improve yield and process stability.

What are popular job titles related to Diffusion Process Engineer jobs in New York? For Diffusion Process Engineer jobs in New York, the most frequently searched job titles are:
What job categories do people searching Diffusion Process Engineer jobs in New York look for? The top searched job categories for Diffusion Process Engineer jobs in New York are:
Infographic showing various Diffusion Process Engineer job openings in New York as of June 2026, with employment types broken down into 2% Locum Tenens, 96% Full Time, and 2% Part Time. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution.

Senior Machine Learning Engineer

Career Renew

New York, NY โ€ข Remote

$165K - $225K/yr

Full-time

Posted 13 days ago


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 DeepStainโ„ข and ReStainโ„ข 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 Pictorโ€™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 PictorLabsโ€™ 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 Masterโ€™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.