Data Scientist

Job Summary We are looking for a highly capable engineer/researcher to lead the R&D of Small Language Models (SLMs) and Vision-Language Models (VLMs) for edge / low-latency and cost-efficient production scenarios. You will own the continuous pretraining, supervised instruction tuning (SFT), and compression/distillation pipelines, and work closely with platform teams to deliver reliable, measurable improvements in inference efficiency, tool-use success rate, and overall model quality. Key

• SLM/VLM Training: Continuous Pretraining & Instruction Tuning (SFT) • Conduct continuous pretraining and SFT for SLMs and VLMs to improve task performance and domain adaptation. • Build reproducible training workflows in PyTorch, including data processing, training, evaluation, and model versioning. 2) Compression, Distillation & Edge/Low-Latency Inference Optimization • Design and implement efficient compression strategies for SLM/VLM, including knowledge distillation, pruning, and quantization-oriented training or post-training optimization. • Optimize model serving and inference for low-latency / edge scenarios by improving throughput and cost-per-token via techniques such as quantization, caching/KV optimizations, batching strategies, and decoding-time optimizations. 3) Tool Calling System: Catalog, Routing, Validation, Fallback & Observability • Architect and implement a production-grade tool calling (function/tool calling) framework: • Tool cataloging and metadata/sc