Essential Qualifications:
Possession of a PhD in Computer Science or a closely related discipline, which may be completed or currently in progress.
Authorship of two or more publications presented at leading Vision/ML conferences, specifically: CVPR, ICCV/ECCV, Neurips, ICML, or ICLR.
Proficiency in working with deep generative models or Gen-AI technologies.
Practical experience with neural radiance fields (nerf) or gaussian splatting techniques.
Strong command of the Python programming language.
Please Note: Candidates lacking all the specified qualifications will not be eligible for consideration for this role.
Position Availability:
This position is fully remote.
Candidates located in the U.S. and Canada must be capable of employment on a TEKsystems W-2 without the necessity for future sponsorship.
Description:
Lead and execute research on Gen-AI or 3D reconstruction problems in the field of ML/AI to generate 3D content.
They are able to push the boundaries of areas within the subfields listed above for synthetic data research.
Lead the research on both the short term plans that align with the team and long term ambitious research goals.
Collaborates with other researchers and engineers in a dynamic environment.
Follow the latest deep learning research and guide the team discussion regarding R&D priorities.
Publish academic papers, XFN reports and blog posts showcasing our algorithmic capabilities and/or the value of our data
About TEKsystems:
We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia. As an industry leader in Full-Stack Technology Services, Talent Services, and real-world application, we work with progressive leaders to drive change. That's the power of true partnership. TEKsystems is an Allegis Group company. The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.