Siddharth Madan
Backend EngineerStaff Deep Learning Engineer
Rivian
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Staff Deep Learning Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
My background is in computer vision and machine learning, and I enjoy working in roles that leverage CV/DL techniques for real world product development. My work over recent years has focused on developing CV/DL based methods for autonomous driving.
Rutgers University
Doctor of Philosophy (PhD), Computer Vision
N/A – Present
Rivian
Staff Deep Learning Engineer
December 1, 2023 – Present
Rivian
Senior Deep Learning Engineer
February 1, 2022 – December 1, 2023
Argo AI
Software Engineer, Machine Learning
August 1, 2018 – January 1, 2022
Palo Alto, CA
Volvo Car Group
Deep Learning Engineer
April 1, 2017 – July 1, 2018
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FeatureX
Research Scientist
August 1, 2016 – March 1, 2017
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Aware, Inc.
Research Scientist
March 1, 2013 – August 1, 2016
Bedford, MA
Cultural Fit Analysis
The candidate has a strong background in specialized Deep Learning and Computer Vision roles within the automotive and satellite imagery sectors. While this demonstrates deep expertise in a niche, the target role of 'Backend Engineer' might require a broader set of backend development skills (e.g., distributed systems, API design, database management, cloud platforms) that are not explicitly highlighted in their experience. The focus has been heavily on ML model development and deployment rather than general backend infrastructure. This specialization might lead to a moderate cultural fit for a general backend engineering role, but a strong fit for an ML-focused backend role.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight independent algorithm development and production implementation, suggesting strong problem-solving and execution skills. The work on safety-critical systems implies attention to detail and robustness. However, direct evidence of team collaboration, stress handling, or communication clarity in a team setting is not explicitly detailed in the provided experience descriptions.