Minimum qualifications:

• PhD degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
• Experience with Signal and Power Integrity (SI/PI) fundamental concepts, microwave theory, or analog circuit design.

• Domain Skills:
• Solid grasp of foundational generative technologies, including LLMs and diffusion models, as well as a deep understanding of deep learning and machine learning in general
• Comprehensive understanding of the machine learning pipeline, encompassing data processing, training, and performance analysis. Favor the adoption of reproducible and scientifically rigorous experimental processes to enhance the reliability and validity of your work.
• Knowledge of distributed systems and their application to machine learning.
• Strong analytical and problem solving skills, with emphasis ofon software debugging of complex ML code basis
• High fluency in Python and ability to navigate in C/C++/ObjC/Swift code bases
• Engineering skills:
• Experience in structuring software development teams
• Experience in building scalable and maintainable software operating on large datasets and with a high degree of task parallelism
• Familiarity with evolving ecosystems and with the infrastructure integration challenges
• Familiarity with the challenges of developing algorithms and models that eventually run on embedded hardware
• Soft skills:
• Excellent written and verbal communications skills
• Excellent problem-solving and project management skills
• Ability to interact with parties with varying levels of technical proficiency
• Ability to work hands-on in multi-functional teams
• Demonstrated capacity to build solid relationships across organizations and functions (R&D, privacy and legal, tools & infrastructure…)
• Ambitious and willing to continuously grow your technical background