The Role
Lead and scale an AI engineering team to design, train, optimize, and deploy deep learning models for 2D/3D medical imaging. Oversee model architecture, MLOps pipelines, clinical integrations (PACS/RIS), and optimize models for cloud, edge, and clinical hardware while mentoring engineers and ensuring regulatory and data privacy considerations.
Summary Generated by Built In
Work Flexibility: HybridPosition OverviewWe are seeking a visionary Technical Manager with 15+ years of overall experience to lead our AI engineering team. You must bring 5 years of dedicated experience managing, mentoring, and scaling technical teams. In this role, you will drive the development, training, and deployment of cutting-edge AI models, with a specific focus on medical imaging applications. This role balances technical architecture, hands-on modeling strategy, and people leadership.What you will do:
- Lead the end-to-end strategy for AI model development and production deployment.
- Evaluate and implement emerging AI frameworks, tools, and cloud methodologies.
- Oversee the training and optimization of deep learning models for imaging computer vision.
- Guide the team in handling 2D/3D medical modalities like MRI, CT, X-ray, and Ultrasound.
- Deep knowledge of deep learning for segmentation, detection, classification, registration, reconstruction, and longitudinal change analysis
- CNNs, Transformers, U-Net variants, nnU-Net, and foundation/self-supervised models for imaging.
- Architect robust MLOps pipelines for continuous model monitoring, testing, and deployment.
- Optimize models for cloud, edge, and clinical environment hardware constraints.
- Master’s or Ph.D. in Computer Science, Biomedical Engineering, or a related field.
- 15+ years of experience in software engineering and data science.
- 5 years of direct experience managing and leading technical AI/ML teams.
- Strong research background with demonstrated contributions in AI/ML through publications, patents, applied research, industrial innovation, or equivalent scientific work.
- Deep knowledge of Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Large Language Models, Agentic AI / AI Agents
- Proven experience developing advanced AI models from research through implementation and evaluation.
- Core AI: Deep understanding of CNNs, Transformers, segmentation, and object detection.
- Imaging Libraries: Expertise in DICOM, NIfTI, ITK, Monai, OpenCV, and PyTorch/TensorFlow.
- MLOps: Hands-on experience with Docker, Kubernetes, Triton, AWS, GCP, or Azure ML.
- Data Handling: Experience with medical data de-identification, curation, and active learning.
- Strong communication skills to bridge the gap between technical teams and medical experts.
- Proven track record working with medical imaging data and clinical workflows.
- Knowledge of clinical workflow integration: PACS/RIS/VNA, DICOM networking, study routing, and integration with hospital IT systems
- Designed scalable infrastructure for processing high-resolution medical imaging datasets.
- Ensured compliance with medical software standards, data privacy, and security protocols.
Skills Required
- Master's or Ph.D. in Computer Science, Biomedical Engineering, or related field.
- 15+ years of experience in software engineering and data science.
- 5 years of direct experience managing and leading technical AI/ML teams.
- Strong research background with publications, patents, applied research, or equivalent scientific contributions.
- Deep knowledge of Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Large Language Models, and Agentic AI.
- Proven experience developing advanced AI models from research through implementation and evaluation.
- Deep understanding of CNNs, Transformers, segmentation, detection, classification, registration, and reconstruction methods.
- Experience with imaging libraries and formats: DICOM, NIfTI, ITK, Monai, OpenCV.
- Experience with PyTorch and/or TensorFlow.
- Hands-on MLOps experience with Docker, Kubernetes, Triton, and cloud ML platforms (AWS, GCP, or Azure ML).
- Experience with medical data de-identification, curation, and active learning workflows.
- Strong communication skills to bridge technical teams and medical experts.
- Proven track record working with medical imaging data and clinical workflows.
- Knowledge of clinical workflow integration: PACS, RIS, VNA, and DICOM networking.
- Experience designing scalable infrastructure for high-resolution medical imaging processing.
- Ensured compliance with medical software standards, data privacy, and security protocols.
Inari Medical Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Inari Medical and has not been reviewed or approved by Inari Medical.
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Fair & Transparent Compensation — Pay is considered competitive for many roles, with employees described as 'extremely happy' with total compensation including pay, stock, equity, and benefits. Feedback suggests above‑average salary levels across departments contribute to strong pay sentiment.
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Healthcare Strength — Benefits include comprehensive medical, dental, and vision coverage, with 100% employer‑paid premiums for employees cited in multiple descriptions. Additional options like critical illness, accident, hospital indemnity, and wellness programs reinforce the healthcare offering.
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Leave & Time Off Breadth — Time off is characterized by unlimited PTO, cited as a top benefit by employees. Paid holidays and flexible hours are also referenced, expanding the range of time‑off options.
Inari Medical Insights
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The Company
What We Do
Inari Medical, Inc. is a commercial-stage medical device company focused on developing products to treat and transform the lives of patients suffering from venous diseases.
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