ML Systems Engineer · Distributed Training · LLM Infrastructure · Multimodal Systems
I build systems that work at scale -- and try to understand why they fail when they do.
I work in the space between clean research ideas and the messy reality of clusters that fail, data that drifts, and models that need to stay honest in production.
Day-to-day: cloud-scale ML infrastructure, distributed training systems, fault-tolerant checkpointing, LLM safety and observability layers, and the occasional low-level kernel when something needs to be faster or more reliable. The majority of that work lives in private repositories. What you see here are the side projects I chose to open-source because they felt worth sharing.
Things I care about technically
- Large-scale pre-training infrastructure -- MoE routing, fault-tolerant checkpointing, tensor/pipeline parallelism
- LLM safety and observability -- keeping models (and the agents built on them) honest at inference time
- The hardware-software boundary: SIMD, CUDA, kernel-level optimization
- Novel architectures worth deploying, not just benchmarking
Things I care about less technically
- Code that impresses interviewers but breaks on week two
- Benchmarks that only win on synthetic data
- Documentation that describes the happy path and nothing else
These are small but high-signal fixes in core infrastructure:
| Project | Status | What was fixed | Link |
|---|---|---|---|
| Triton | Merged | Fixed NaN handling in tl.argmin / tl.argmax so interpreter matches JIT behavior |
PR #10699 · write-up |
| TensorFlow | Merged | tf.experimental.numpy.swapaxes now raises a clear error on out-of-bounds axis instead of silent normalization or opaque XLA errors |
PR #122544 · write-up |
| Megatron-LM | Merged | Fixed crash in get_grad_norm_fp32 when gradient list is empty (common with frozen layers / tensor parallelism) |
PR #5530 · write-up |
I'd rather have a few things that are real than many that just look good on a profile.
| Project | What it is | Where it actually stands |
|---|---|---|
| KANX | Production-oriented Kolmogorov-Arnold Networks library (PyTorch + TensorFlow + ONNX) | pip install kanx ·  · Colab · write-up · DOI |
| Composed-MoE-Engine | Fault-tolerant MoE training runtime with fused Triton router, 4D parallelism, elastic recovery, and async checkpointing | 80.1× (T4) / 58.7× (A100) kernel speedup · 348 tests · three hardware-only bugs documented · multi-GPU validation in progress · DOI |
| guardrail-rs | Zero-Python LLM security reverse proxy in Rust (prompt injection, PII redaction, policy enforcement) | Sub-ms overhead · fails open by design · six real bugs written up · Colab · write-up |
| FlashSpec | Adaptive speculative decoding with online bandit draft selection and Triton verification | pip install flashspec ·  · notebooks |
| PromptCanary | Detects silent behavioral drift in LLM providers | pip install promptcanary ·  · notebooks |
- RAG-Multimodal-Financial-Doc-Analysis-and-Recall -- Multimodal RAG for financial documents (hybrid retrieval, VLM chart understanding, numeric grounding)
- production-vlm-engineering -- Reproducible pipelines for modern multimodal vision systems
- mcp-reliable -- Runtime observability for MCP servers (195 tests, none mocked at protocol level)
- ReliableAgent -- Reliability-first agent orchestration
Training & inference -- PyTorch TensorFlow Triton ONNX TensorRT FSDP2
LLM -- Transformers PEFT vLLM FastAPI
Infra -- NCCL Kubernetes Helm Ray
Observability -- Prometheus Grafana OpenTelemetry
Low-level -- C++ CUDA SIMD pybind11
Most of my interesting work happens in private repositories. This GitHub is a public window, not the full picture.
The repositories here are written to the same standard I use privately: tests, type checking, CI, real (if limited) benchmarks, and documentation that tries to admit what doesn’t work yet.
My path into this wasn’t linear. It started in mechatronics -- building systems with real sensors, actuators, and control loops that fail in ways no unit test catches -- and later working on ML systems inside industrial control and safety environments, where a silent failure has real consequences. That background is the root of the reliability focus you see in these projects.
- Working on: the MoE engine’s chaos scenario A -- sudden node failure under expert resharding (currently recovering ~85% of the time)
- Reading: the Megatron-LM codebase and the FlexAttention paper
- Thinking about: whether MFU tracking gives enough signal to catch silent training degradation early
Outside of work I’m usually reading something I don’t fully understand yet, listening to music that has no business being that good, and occasionally wondering if the model actually converged or if I just got lucky. I like working with people who say “I don’t know” without embarrassment and argue about architecture in good faith.