Algorithms Engineer – Mapping Systems, Autonomous Vehicles Team
At the Samsung Strategy and Innovation Center (SSIC), we discover and develop groundbreaking technologies to help people around the world lead happier, healthier, richer lives.
Founded in 2012 and led by Samsung Electronics President and Chief Strategy Officer, Young Sohn, SSIC works with entrepreneurs and strategic partners to foster disruptive technologies, invest in promising startups, and build new business lines through M&A and strategic partnerships. Our work spans across a range of technologies, including artificial intelligence, autonomous mobility, digital health, Internet of Things, cloud data infrastructure, privacy, and security.
Through our commitment to developing innovative, reliable products and services, recruiting top-tier talent, upholding the responsibilities of global citizenship, and collaborating with our partners and customers, SSIC is imagining the technologies that will reshape the world.
SSIC has offices in Menlo Park and San Jose, California, with additional locations in Paris, France; Tel Aviv, Israel; and Seoul, South Korea.
The mapping systems team is seeking a software engineer specializing in algorithms and data structures, to help us solve problems in a high-fidelity, high-frequency environment. Some applications require analysis of large scale data that demand clever algorithmic techniques to help us solve some seemingly simple problems that become challenging due to scale. Ability to analyze the input/problem domain, understand and be able to exploit the nuances to develop highly efficient practical combinatorial and numerical algorithms. In their capacity as an algorithms engineer, the candidate will have a chance to work across various parts of the stack in the autonomous driving system, and across various teams. The candidate will have a unique chance to develop their career in various ways.
- Masters/PhD in the area of combinatorial and/or numerical algorithms, or related field.
- Practical (near-linear/sub-linear) combinatorial algorithms.
- Strong background in fundamental algorithmic techniques for graphs, sequences and other standard structures, and data structures.
- Good grasp of probabilistic algorithms and standard techniques.
- Strong grasp of fundamental linear algebra concepts and their applications.
- Reasonably skilled in C/C++; a reference to a github project will assist in assessment.
- Strong background in discrete and numerical (approximation) algorithms.
- Background in computational geometry.
- Git, Cmake, Boost, and CGAL.
- Experience with standard machine learning techniques.
- Experience with standard convex optimization tools.
- Basic understanding of standard mapping projection schemes.
- Background in scientific computing and numerical analysis.