About the job offer

Neuralk-AI is looking for a AI Engineer to build our AI systems and work hand in hand with our science team to accelerate research.

Join our Research & Development team to advance our Applied-AI initiatives.

Key Responsibilities:

• Accelerate research by developing tools to automate complex workflows.
• Bridge the gap between our research team and client data, ensuring seamless integration.
• Conduct cutting-edge research on the latest AI and machine learning technologies.
• Engineer, design, implement, and improve highly-scalable AI models and algorithms, while executing rigorous experimental protocols.
• Write clean, efficient, and scalable machine learning code.
• Create prototypes and proof-of-concept models to explore AI application potential.

Minimum Qualifications:

• Master's degree in Computer Science, Computer Engineering or relevant technical field
• Proven work experience in a university or industry lab(s), in a role with primary emphasis on AI research and integration (transformers/GNN).
• Broad knowledge of the field of AI, and specific knowledge of interest in training and fine-tuning AI models (transformers / GNNs) with a focus on architecture development, model evaluation and usage.
• High technical engineering competence. This means being able to design complex software and make them usable in production.
• Being able to navigate the MLOps technical stack.
• Being able to use complex HPC infrastructure with full autonomy.
• Hands-on experience with AI frameworks and tools (e.g., Sklearn, PyTorch, Jax).
• Experience working with large distributed systems.
• Self-starter and autonomous.
• Low-ego.
• Collaborative and have a real team player mindset.

Preferred Qualifications:

• 2+ years of work experience in both a faculty and an industry research position with primary emphasis on AI research
• 2 to 5 years of work experience in a fast growing environment (e.g. start-up or scale-up)
• Demonstrated machine learning experience in one of the following: open-source activity, data science competitions.
• Experience in creating high-performance implementations in deep learning frameworks (PyTorch, CUDA) at scale.
• Experience with distributed systems or on-device algorithm development
• Track record of translating research into business impact
• Experience in developing and debugging in C/C++, Python