Help & Supportcareers@gravityer.com
AI/ML Engineer | Superhuman
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Master’s degree with 10 years experience in software & data engineering, machine learning, natural language processing and generative AI.
Stanford University
Master's Degree, Management Science & Engineering
N/A – Present
Carleton University
Bachelor of Engineering (B.Eng.), Biomedical & Electrical Engineering, Minor in Computer Science
N/A – Present
Columbia University
Certificate, Business
N/A – Present
Superhuman
AI/ML Software Engineer
April 1, 2025 – Present
San Francisco Bay Area
Coda
AI Engineer
June 1, 2023 – April 1, 2025
San Francisco Bay Area
Affirm
Senior ML Engineer
August 1, 2021 – April 1, 2023
San Francisco Bay Area
8x8
Senior ML Engineer
April 1, 2018 – July 1, 2021
San Francisco Bay Area
MarianaIQ
Data Science Engineer
November 1, 2014 – March 1, 2018
San Francisco Bay Area
Ciena
Hardware Engineer
May 1, 2011 – August 1, 2013
Ottawa, ON
Open Water Diver
PADI
June 24, 2026 – Present
Certificate in Piano Grade 10 Practicum
The Royal Conservatory of Music
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has worked in diverse environments, from early-stage startups (MarianaIQ, Coda) to established companies (Affirm, 8x8, Superhuman), indicating adaptability to different company cultures. The consistent focus on AI/ML roles aligns well with an ML Engineer target role. The breadth of experience in different ML domains (NLP, Generative AI, risk modeling) suggests a versatile individual who can contribute to various aspects of an ML team. The education from top-tier universities (Stanford, Columbia) also points to a strong academic foundation.
Soft Skills & Operational Fit
The candidate's experience descriptions are concise and highlight key technical contributions. The progression through various ML-focused roles suggests adaptability and a continuous learning mindset. The lack of specific project details or behavioral assessment data makes it difficult to fully assess soft skills like teamwork, problem-solving approach, or leadership potential beyond what can be inferred from role progression.