Professional Experience

Building ML/AI products and data-driven systems in big tech and small tech contexts

Apple

Controls Engineer

Aug 2025 – Present Cupertino, CA
  • Working on a team of 7. Focused on manufacturing, algorithms, embedded systems, and force based dynamic control.

Ford Motor Company

Technical Product Manager, AI‑ML Intern

May 2025 – Aug 2025 Palo Alto, CA
  • Collaborated with a team of 40+ ML engineers to develop an in-house demo prototype to evaluate architecture options, providing critical insights that shaped executive platform decision-making on a $200M contract.
  • Designed and deployed a custom embedded small language model (SLM) with cloud fallback for Ford's next-generation Vehicle Assistant, enabling real-time, context-aware in-vehicle interactions using live CAN bus and structured API calls.
  • Evaluated and optimized the assistant's agentic architecture, leveraging retrieval-augmented generation (RAG) for dynamic knowledge injection and building data-driven routing success metrics using BigQuery.

Trilobio

Electrical Engineering Intern

Aug 2024 – Dec 2024 San Francisco, CA
  • Built an automated mass sensing prototype, combining capacitive sensing, flexure mechanics, and robotic control for high-throughput lab automation.
  • Designed and fabricated parallel plate and fringe capacitor PCBs using KiCad for differential capacitance measurement in high-resolution mass sensing applications.
  • Developed viscoelastic damping systems for flexible polymer fixtures, reducing vibrational noise and enabling 0.1 mg measurement resolution.
  • Integrated 24-bit ADCs, embedded signal conditioning, and RF shielding and grounding techniques to ensure signal integrity in noisy environments.

Ford Motor Company

Data Science Intern

May 2024 – Aug 2024 Palo Alto, CA
  • Integrated Ford's protobuf-based Vehicle Energy Model (VEM) onto the V363 EV platform, enabling energy-aware routing via Android Auto for 20,000+ E-Transit vans.
  • Queried and analyzed fleet-scale telemetry using SQL to validate VEM predictions against real-world drive and charge data, enabling reliable State of Charge (SOC).
  • Architected forward-compatible VEM tuning workflows, performing hyperparameter optimization on weighted model parameters to improve prediction stability across diverse EV usage patterns.
  • Coordinated with Apple CarPlay UX and SYNC engineering teams to streamline the EV pairing and routing experience for 200,000+ Electric vehicles.