Help & Supportcareers@gravityer.com
ML/DL Engineer · Training models, breaking pipelines, fixing them again
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
nyc-rodent-inspection-analytics
June 25, 2026 – Present
PySpark ETL + ML classifier (RF/GBT, AUC 0.87) + interactive dashboard for 2.72M NYC rodent inspection records
View Projectnyc-urban-spatial-analysis
June 25, 2026 – Present
NYC Urban Spatial ETL Pipeline: QA/QC, GeoJSON generation, and interactive Leaflet.js map visualization
View Projectnyu-av-command-center
June 18, 2026 – Present
NYU AV Command Center — Operational dashboard prototype for AV & Media Services
View Projectdoclens
May 29, 2026 – Present
A document-centric RAG web app where users upload PDFs/text files, ask questions, and receive AI-generated answers grounded in the document with source highlighting — answers visually connect back to specific passages.
View ProjectReservaDirect
May 11, 2026 – Present
Autonomous AI-powered restaurant reservation system that books tables anywhere in the world using only your Google Calendar. ReservaDirect turns your calendar into a personal concierge that can call restaurants on your behalf, confirm bookings, and keep your schedule updated in real time.
View Projectmlops-forkwise
April 26, 2026 – Present
ML-powered ingredient substitution system built on top of Mealie, a self-hosted recipe manager. Users browse recipes in Mealie and get smart substitution suggestions for any ingredient — powered by GISMo (Graph-based Ingredient Substitution Module) with ONNX serving and sentence-transformer embeddings.
View Projectkaggle-dl-pixels-to-predictions
April 25, 2026 – Present
Pixels to Predictions is a deep learning vision challenge focused on scientific multiple-choice reasoning. The project fine-tunes a SmolVLM-500M-Instruct model to combine image inputs with question context and predict correct answers, with performance evaluated by test-set accuracy.
View ProjectCultural Fit Analysis
The candidate demonstrates a strong passion for data science and machine learning through numerous personal projects. The projects cover a range of applications from ingredient substitution to urban spatial analysis and deep learning, indicating a broad interest and ability to work across different domains. This aligns well with a culture that values innovation and continuous learning. However, the lack of team-based projects or formal work experience makes it challenging to fully assess collaboration and professional cultural fit.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven individual capable of taking complex ideas from conception to implementation. The diversity of projects suggests adaptability and a willingness to explore different problem domains. However, without formal work experience or psychometric test results, it is difficult to assess specific soft skills like teamwork, stress handling, or communication clarity in a professional setting.