مهندس تعلم الآلة

Roles and responsibilities

As a seasoned Machine Learning Engineer specializing in GPU optimization and large-scale deep learning, you'll play a crucial role in advancing biomedical research through foundational models and cutting-edge AI. Joining a forward-thinking team, you’ll contribute to the development, application, and integration of advanced machine learning methodologies, particularly in foundation models, representation learning, large language models, and generative AI.

Key Responsibilities:

1. Foundation Models & Research Collaboration:
   Your work will contribute to creating the core frameworks for applying foundation models in biomedical research. You'll be involved in projects that aim to harness generative AI and large language models for both internal and external applications, collaborating closely with research scientists to push the boundaries of what AI can do in this space. Your expertise will be instrumental in driving innovative solutions in areas critical to healthcare and biology.

2. Efficient GPU Utilization & CUDA Expertise:
   As an expert in high-performance GPU computing, you'll craft optimized CUDA kernels to fully leverage modern GPUs (like H100) for model training and inference. You’ll maximize performance through low-level code, redesigning architectural elements, and using distributed computing frameworks to handle complex tasks across extensive GPU clusters. These contributions will ensure efficiency in handling massive biomedical datasets and computationally intensive models.

3. MLOps Integration & Performance Optimization:
   Integrating low-level code into high-level MLOps frameworks is key to your role. Your deep knowledge of generative AI and foundation models will be used to streamline deployment processes, allowing seamless scaling in the industry’s fast-paced environment. A solid grasp of Python programming and best practices, paired with experience in frameworks like TensorFlow, PyTorch, or JAX, will be essential as you implement, train, and deploy large neural networks in production.

4. Leadership & Project Management (Dependent on Experience):
   For those with advanced experience, opportunities exist to lead ambitious projects, supervise teams, and manage multi-stakeholder collaborations. You’ll use your organizational and analytical skills to solve complex problems and ensure projects align with company goals, advancing foundational model applications in the biomedical field.

Desired candidate profile

Candidate Profile:
The ideal candidate is proactive, detail-oriented, and thrives in collaborative, fast-paced settings. This role demands not only technical expertise but also curiosity and a dedication to making impactful contributions. Key qualifications include:

• GPU & CUDA Expertise: Profound experience in optimizing GPUs, particularly crafting CUDA kernels for high performance.
• Distributed Computing: Skilled in frameworks to manage GPU clusters efficiently.
• Generative AI & Large Language Models: Demonstrated experience with generative AI, especially in deploying or fine-tuning large language models for practical applications.
• Analytical & Coding Proficiency: Advanced coding skills in Python, along with strong analytical capabilities to solve challenging problems systematically.
• Relevant Education: An MSc or PhD in a related field such as machine learning, computer science, or applied mathematics, or equivalent practical experience.

Additional Skills (Preferred):
Though not required, the following experiences can enhance your candidacy:

• Biological Data Analysis: Familiarity with analyzing biological data, such as proteins, omics, or histopathology.
• Research & Publication Track Record: Experience with high-impact publications and involvement in significant research projects.