Machine Learning Engineer Resume Adrian Bishop Machine Learning Engineer (847) 555-0186 adrian.bishop@example.com linkedin.com/example/adrianbishop Evanston, IL 60201 STRENGTHS Reproducible delivery Turned notebook work into modular Python pipelines with recorded inputs, clear configuration, and repeatable results for classmates. Practical model judgment Compared evaluation tradeoffs with faculty sponsors; conversations became clearer once false alerts and missed delays had names. Collaborative learning Led study sessions and lab discussions where peers brought difficult modeling questions and left with workable next steps. Testing discipline Added pytest, schema checks, linting, and container builds; small checks caught issues before demonstrations and reviews. Clear technical writing Recorded data versions, hyperparameters, limitations, and results so research partners could follow decisions without extra meetings. SKILLS Python pandas scikit-learn PyTorch TensorFlow Airflow Git pytest Modular code CI/CD Docker Cloud services Data preprocessing Feature engineering Model evaluation LANGUAGES English Native 40 Spanish Intermediate 20 MY CAREER 1.5 Years Machine Learning Engineering Project Lead at Northwestern University Machine Learning Project (3 Months) Machine Learning Research Assistant at University Research Lab (7 Months) Machine Learning Engineer Capstone Student at Northwestern University Capstone Project (8 Months) SUMMARY Early-career Machine Learning Engineer with a 2026 B.S. in Computer Science and practical experience building reproducible models, preprocessing workflows, feature-generation systems, and monitored prototypes. Python, pandas, scikit-learn , PyTorch, TensorFlow, Git, pytest, modular code, and model evaluation support reliable delivery from raw data through validated predictions. Academic research and capstone work covered Airflow scheduling, Docker packaging, CI/CD checks, cloud experimentation, experiment documentation, and production-style deployment. Clear success metrics helped align faculty, classmates, and simulated operations stakeholders around useful outcomes. Eager to join Northstar Vector Labs , contribute thoughtfully with engineering partners, and turn sound ML methods into dependable systems that serve real users. EDUCATION B.S. in Computer Science Northwestern University GPA: 3.8 2026 Evanston, IL Coursework: Python, machine learning, data structures, software engineering, statistics, cloud computing TECHNICAL SKILLS Programming Languages: Python, SQL, Bash Data Processing: pandas, NumPy, data validation Machine Learning: scikit-learn, regression, classification Deep Learning: PyTorch, TensorFlow, neural networks Workflow Orchestration: Airflow, DAGs, scheduled tasks Version Control: Git, GitHub, branching Testing Tools: pytest, unit testing, linting Containerization: Docker, Dockerfiles, container builds Cloud Services: AWS, object storage, cloud sandbox Model Evaluation: cross-validation, precision, recall Deployment Practices: CI/CD, batch inference, model serving ML Operations: model monitoring, retraining, experiment tracking SKILLS Python pandas scikit-learn PyTorch TensorFlow Airflow Git pytest Modular code CI/CD Docker Cloud services Data preprocessing Feature engineering Model evaluation EXPERIENCE Machine Learning Engineering Project Lead Northwestern University Machine Learning Project September 2024 - December 2024 Evanston, IL Led a faculty-sponsored ML project from public transit data ingestion through reproducible evaluation, guiding modular development, stakeholder decisions, and presentation delivery. Work connected model quality with operational choices around delay alerts and continued monitoring research. Built scikit-learn pipeline modules for 42,000 transit records, improving reproducible model development. Resolved missing timestamps through pandas profiling, creating interpretable time-based route features. Compared logistic regression, random forest, and gradient boosting with stratified cross-validation metrics. Added Git version control and pytest checks, allowing classmates to reproduce experiments consistently. Defined delay-alert thresholds with faculty sponsors, balancing false alerts against missed delays. Presented evaluation findings to 20 reviewers, earning approval for continued monitoring research. Machine Learning Research Assistant University Research Lab January 2025 - August 2025 Evanston, IL Supported microscopy research through PyTorch workflows, scheduled preprocessing, containerized training, cloud artifact storage, and clear experiment records. Contributions helped faculty and graduate researchers repeat runs, inspect errors, and discuss practical model limits. Developed PyTorch image classification workflow for 18,000 microscopy images, supporting repeatable research runs. Scheduled preprocessing and feature tasks with Airflow , recording inputs and parameter settings. Reviewed held-out validation and learning curves, identifying overfitting across image classes. Containerized training dependencies with Docker , reducing setup differences across four lab workstations. Stored experiment artifacts in cloud object storage, preserving data versions and hyperparameters. Coauthored research presentation translating model limits into practical testing recommendations for faculty. Machine Learning Engineer Capstone Student Northwestern University Capstone Project September 2025 - May 2026 Evanston, IL Designed a production-style demand forecasting service for simulated retail operations, connecting pandas preparation, scikit-learn training, Docker deployment, CI/CD validation, and model monitoring . Coordinated success measures with an operations stakeholder and documented decisions for dependable use. Designed demand forecasting service with pandas preparation and tested scikit-learn training components. Created feature modules for promotions, seasonality, inventory history, and product groups. Validated schemas, nulls, and leakage risks before training models across product categories. Packaged batch prediction service with Docker, documenting deployment steps for a cloud sandbox. Added Git-based CI checks for tests, linting, schemas, and container builds. Set mean absolute error targets with operations stakeholders , guiding monitoring and retraining decisions. PROJECTS Cloud Model Monitoring Sandbox May 2026 Created a separate cloud sandbox for batch prediction monitoring, artifact review, alert logic, and retraining notes. Project connected deployment records with practical model-performance checks. Airflow Feature Pipeline Study August 2025 Built a scheduled Airflow study pipeline for dataset checks, feature generation, parameter capture, and experiment records. Work focused on repeatable runs and clear handoffs. LEADERSHIP & AWARDS Dean’s List, Northwestern University, 2024 - 2026 Undergraduate Research Presentation Award, Northwestern University, 2025 Finalist, Midwest Student Data Challenge, 2026 CERTIFICATIONS Machine Learning Specialization, Coursera 2025 AWS Academy Cloud Foundations 2025 TensorFlow Developer Foundations 2026 PROFESSIONAL AFFILIATIONS Machine Learning Study Group Coordinator, Northwestern University, 2025 - 2026 Teaching Assistant, Introduction to Programming, Northwestern University, 2025 Member, Northwestern Data Science Club, 2024 - 2026 LANGUAGES English (Native) Spanish (Intermediate) ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. REFERENCES AVAILABLE ON REQUEST
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