Software Developer
Building AI/ML systems, LLM applications, and geospatial ML pipelines
Software developer building AI/ML systems and applied ML pipelines, with expertise in machine learning, deep learning, LLMs, geospatial ML, and model interpretability (SHAP, LIME). Experienced across Python, PyTorch, FastAPI, PostGIS, and backend deployment (Nginx, Supervisor) — from data pipelines to production ML systems.
Always up for a conversation on ML systems, model deployment, or applied AI — let's connect!
Contributions:
- TRL Interoperability Adapters — Built zero-overhead adapters bridging TorchRL and Hugging Face
trl, letting teams mix TorchRL's collectors/replay buffers with HF's reward models and trainers without adopting either stack wholesale. Included 17 tests with full E2E coverage and a Sphinx tutorial. Part of the Post-Training RFC (#3948). - Sequence Sampling Unit — Designed and implemented fixed-length sequence sampling with three distinct trajectory-boundary policies (
pad,stop,include_reset), decoupling sequence-range mechanics from sampling strategy so they can be composed independently. - Mask-Aware Loss Reduction Migration — Migrated 14
LossModulesubclasses (PPO, SAC, DQN, TD3, GAIL, IQL, and more) to a mask-aware reduction path, ensuring padded sequence positions are correctly excluded from loss computation — with byte-identical behavior when no mask is present. - Version-Safe Replay Buffer Updates — Implemented optional compare-version semantics for
update_if_present, preventing stale async writes from clobbering newer state in live buffer slots. - Plus 3 additional documentation and bugfix contributions (#3600, #3779, #4102).
Feel free to explore my repositories, star or fork, or drop me a message if you'd like to chat about code, AI, geospatial tech, or machine learning ideas. 🚀