Tianchang Liu
EXPERTISE
BIO
Senior Machine Learning Engineer with 10+ years of experience building and deploying scalable AI systems across computer vision, NLP, generative AI, and MLOps. I've led end-to-end ML projects from real-time edge inference to LLM-powered applications, with a focus on performance, reliability, and impact. My work spans startups and enterprises, including Mashgin, Gensyn, Voyage, and Amazon. Skilled in Python, Go, Kubernetes, TensorRT, DeepSpeed, and cloud infrastructure, I specialize in shipping production-grade AI that solves real-world problems.
WORK EXPERIENCE
Senior Machine Learning Engineer
Mashgin
June 2021 - Present
- Computer Vision & Generative AI · Improved SKU recognition accuracy to 98.5% for 10K+ products using EfficientNet-B7/ViT models · Deployed quantized models (TensorRT/ONNX Runtime) on NVIDIA Jetson Xavier; reduced inference latency 500ms → 120ms · Built 3D synthetic data pipeline (Blender + ComfyUI); generated 100K+ images/year, cutting labeling costs by $250K/year · Implemented NeRF asset generation with NerfStudio, boosting novel item recognition by 18% - MLOps & Infrastructure · Built Kubernetes CI/CD with ArgoCD/Flux, enabling hourly rollouts to 3,700+ edge devices · Designed Feast feature store w/ Redis cache; reduced real-time feature latency 200ms → <50ms · Managed AWS infra (EC2, SageMaker, S3) with Terraform; achieved 99.99% uptime - LLMs & RAG Systems · Built RAG system with Llama-2 (LoRA), LangChain, and Pinecone; reduced support ticket time by 65% · Developed Stable Diffusion workflows (ControlNet, DreamBooth) for on-demand marketing content · Automated product imagery via DALL-E API integration - Data & Microservices · Handled 15TB/day transaction data via PySpark + Delta Lake on Databricks · Streamed events using Apache Kafka + Avro schemas · Built Node.js microservices w/ WebSockets for live inventory updates · Designed HIPAA/GDPR-compliant pipelines for healthcare clients - Financial Systems · Engineered Go-based gRPC payment router handling 5M+ transactions/day @ <10ms latency · Implemented chaos testing (Gremlin) and performance profiling (pprof)
AI Researcher
Gensyn
January 2020 - May 2023
· Designed Verde protocol for decentralized ML validation using Rust, Solidity, ZK-SNARKs (Circom) · Built RL Swarm framework using Ray, PyTorch, and Horovod to scale P2P RL to 10K+ GPUs · Fine-tuned LLaMA-1/2 with DeepSpeed ZeRO-3 and LoRA adapters · Developed RAG pipelines using Hugging Face, LlamaIndex, and Weaviate for 10M+ research papers · Built Go-based P2P layer (libp2p) and deployed IPFS-based model storage (5× faster than S3) · Automated synthetic training data via Stable Diffusion + BLIP-2
Machine Learning Engineer | MLOps Lead
Voyage
January 2017 - March 2021
· Trained YOLOv3/v4 and Mask R-CNN; improved pedestrian detection mAP by 15% · Optimized LiDAR (PointNet++, CUDA); reduced perception latency by 30% · Managed 50TB+/month data via Spark pipelines on Databricks · Built Airflow DAGs (KubernetesPodOperator) for auto-retraining · Deployed TFLite models with <50ms inference on AV fleets · Extended CARLA/Deepdrive with custom UE4 plugins; built C++ kernels for real-time fusion
Machine Learning Engineer | Full-Stack Developer
Startup X
January 2014 - December 2016
· Augmentus (Robotics): Developed ROS-based path planning and WebGL (Three.js) visualization · XpertFlow (Healthcare AI): Deployed ECG classifiers (TensorFlow, 94% AUC), HIPAA-compliant DB design · StaffAny (SaaS): Built scalable Node.js + React system; used RabbitMQ for async workflows
EDUCATION
Master of Science
University of Southern California, September 2014
Mathematics And Computer Science
Bachelor of Science
Tsinghua University, September 2012
Computer Science
Verified Machine Learning Engineer
8+ years of experience
Preferred commitment: Full Time
Languages
Skills
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