MAYA LAIDLER

Selected for MLH's Production Engineering Fellowship hosted with Meta — building and operating reliable, production-grade software with an emphasis on deployment, testing, and system reliability.

• Built and shipped a full-stack Flask + MySQL application, deploying it to an Ubuntu Linux VPS and serving it as a systemd service so it restarts automatically on boot and survives crashes.
• Designed a timeline REST API backed by a peewee ORM data model, adding request validation and a test-mode SQLite configuration so the test suite runs without a live database.
• Wrote a Python unittest suite and a curl-based integration script covering create/read flows and malformed-input edge cases, catching regressions before each deploy.
• Automated deployment with a bash script that syncs the latest main branch, reinstalls dependencies, and restarts the service — collapsing a multi-step manual deploy into a single command.
• Practiced core production-engineering workflows: environment and secret management, service logs and monitoring, and Git-based code review with fellow engineers.

Supported IT and security operations for a regulated national-laboratory environment.

• Designed and deployed Splunk dashboards monitoring 250+ endpoints for real-time system-health visibility.
• Rolled out Cortex XDR endpoint protection across the network and automated agent installation with scripts, cutting manual setup time.
• Documented processes and collaborated with the plant IT team under strict compliance requirements.

Searched for radio technosignatures — signs of extraterrestrial technology — in large radio-telescope datasets.

• Analyzed MeerKAT radio-telescope data to search for narrowband technosignatures across thousands of stars.
• Built a filtering and candidate-reduction workflow that narrowed 30,000+ raw detections down to 10 high-confidence candidates.
• Presented methods and findings to researchers at the University of Oxford.

Teaching assistant and tutor for core computer-science courses.

• Support Data Structures & Algorithms and Theory of Computation as a teaching assistant.
• Tutor ~30 students weekly — explaining complex concepts, debugging student code, and running review sessions.

Applying machine learning to astrophysical simulations to understand the physics of galaxy formation.

• Apply ML to 3D magnetohydrodynamic (MHD) simulations of the circumgalactic medium to model galaxy formation.
• Trained and compared Ridge regression, Random Forest, and PyTorch neural-network models to predict silicon mass in simulated gas.
• Managed 2TB datasets and ran training jobs on the Midway Supercomputer (HPC cluster).

Remote-sensing research on environmental change in Arctic river deltas.

• Process satellite imagery in Python to study nutrient flux and retention across Arctic river deltas.
• Build reusable remote-sensing pipelines to analyze large geospatial datasets.