# David Qifong Jiang

> Machine Learning Engineer | Software Engineer  | LLMs · Deep Learning Systems · PyTorch · Cloud Infrastructure | Cornell Tech

Location: Jersey City, New Jersey, United States
Profile: https://flows.cv/davidqifongjiang

I’m currently a graduate student at Cornell Tech, pursuing a Master of Engineering (M.Eng.) in Electrical & Computer Engineering (GPA: 3.9/4), with a strong foundation in machine learning and software systems. 

My work spans both research and engineering: I built a PyTorch-style deep learning framework (MiniTorch) from scratch (autograd, broadcasting, stride-aware tensors) and optimized performance (up to ~8× CPU speedup and ~2.3× GPU training speedup). I also developed an end-to-end Text-to-SQL system (T5 fine-tuning + LLM prompting) and designed a Cloud Run + Pub/Sub asynchronous video processing pipeline with idempotent retries. 

I’m looking for an internship opportunity in Data Analysis / Data Science / Machine Learning where I can apply my skills in modeling, evaluation, and deployment (Python, PyTorch, Transformers, SHAP, SQL, Docker, GCP). I’m open to roles across different industries, and I’d love the opportunity to connect.

## Work Experience
### Software Engineer (Backend) @ Cornell Tech
Jan 2025 – Present | Jersey City, New Jersey, United States
Built a cloud-native asynchronous video processing backend to handle long-running transcoding workloads reliably.

• Designed a stateful job pipeline with explicit lifecycle tracking (UPLOADED → PROCESSING → READY / FAILED)
• Implemented idempotent processing and safe retries to prevent duplicate work under Pub/Sub redelivery and partial failures
• Deployed containerized workers on Cloud Run, decoupling user-facing APIs from compute-intensive processing
• Added failure handling, job status updates, and structured logging for debugging + observability
• Tech stack: Docker, Google Cloud Run, Pub/Sub, FFmpeg, Linux (plus your API layer)

### Research Assistant @ King's College London
Jan 2023 – Jan 2024 | London Area, United Kingdom
• Cleaned and preprocessed a large-scale wastewater microbiome dataset; performed feature engineering guided by biological domain knowledge.
• Trained and evaluated 15+ machine learning models to (1) classify wastewater microbiomes, (2) predict treatment efficiency indicators, and (3) forecast microbiome composition under different operating conditions.
• Conducted comparative performance analysis across models to identify approaches that best support wastewater treatment plant efficiency.
• Applied SHAP for interpretability, analyzing feature–target relationships and assessing how microbiome features may contribute to treatment efficiency.
• Built a reusable ML code package (21 models) including standardized preprocessing, hyperparameter tuning, and visualization modules to support repeatable experimentation.
• Co-authoring a paper on Machine Learning in Wastewater Treatment with Jiangwen Dong, David Jiang, Tom Vinestocka, and Miao Guo (manuscript in preparation).


## Education
### Master of Engineering - MEng in Electrical and Computer Engineering
Cornell Tech

### Master of Engineering - MEng in Computer Engineering
Cornell University

### Bachelor of Engineering - BE in Electrical and Electronics Engineering
King's College London

### Bachelor of Science - BS in Electrical and Electronics Engineering
King's College London


## Contact & Social
- LinkedIn: https://linkedin.com/in/david-qifong-jiang-8634a3235

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Source: https://flows.cv/davidqifongjiang
JSON Resume: https://flows.cv/davidqifongjiang/resume.json
Last updated: 2026-04-13