Machine Learning Research Scientist

Job Details:

Machine Learning Research Scientist

We are seeking multiple Machine Learning Research Scientists to join our team immediately. This role is ideal for candidates with strong foundations in advanced machine learning techniques—particularly deep reinforcement learning, medical imaging, and large-scale language and vision models. You will work on cutting-edge AI systems with applications that directly impact real-world outcomes.

Key Responsibilities

• Develop, train, and optimize deep reinforcement learning models for complex, real-time environments.
• Lead and contribute to research on medical image analysis, including segmentation, registration, and image/video tracking.
• Build and refine models using LLMs, LVMs, and NLP architectures.
• Collaborate with cross-functional teams to integrate AI models into production workflows.
• Conduct experiments, analyze results, and document research findings.
• Stay current on emerging AI techniques and recommend improvements to our research strategy.

Required Qualifications

• Proven experience in deep reinforcement learning.
• Hands-on experience with medical image analysis, such as image segmentation, registration, and video/image tracking.
• Strong background working with Large Language Models (LLMs), Large Vision Models (LVMs), and natural language processing (NLP) frameworks.
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow).
• Solid understanding of algorithm design, optimization, and applied machine learning.

Preferred/Bonus Experience

• Experience with any of the following is a significant plus:
• TensorRT optimization
• NVIDIA IGX platform
• 3D visualization programming on Linux
• Jetson systems (deployment and optimization)

What We Offer

• Opportunity to work on high-impact, cutting-edge AI research
• Collaborative environment with leading experts in the field
• Competitive compensation and benefits
• Fast-paced, innovation-driven culture

To apply, email [email protected] your resume.

Sheila Shamloo
[email protected]

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