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 Responsibilities
- 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.
- 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.
- Tool Calling System: Catalog, Routing, Validation, Fallback & Observability
- Architect and implement a production-grade tool calling (function/tool calling) framework:
- Tool cataloging and metadata/schema design
- Tool selection/routing and argument construction
- Parameter validation, result verification, and safe fallback/retry strategies
- Call-chain tracing, monitoring, and observability to improve success rate and ROI
- RL & Reward Modeling for Alignment and Tool-Use Reliability
- Apply post-training methods such as PPO / DPO / GRPO-like optimization and reward modeling to align the model toward objectives including:
- semantic understanding
- tool-use success rate
- content generation quality and consistency
- Support both offline and online iteration loops, including policy evaluation, regression checks, and safe deployment gating.
- Data Pipeline Automation (Collection, Cleaning, Curation)
- Design automated pipelines for data collection, filtering, cleaning, de-duplication, labeling/weak supervision, and dataset version management to continuously improve training quality.
- Ensure datasets support both SFT and preference/RL style post-training.
- Rigorous Evaluation, Testing & Iteration
- Build robust evaluation mechanisms: offline benchmarks, task suites for tool-use, regression tests, and reliability metrics.
- Drive rapid iteration through A/B comparisons, ablations, and failure analysis, improving both quality and efficiency over time.
Required Qualifications
- Strong software engineering skills in Python and C++, including experience building ML training/evaluation pipelines in PyTorch.
- Hands-on experience in model efficiency and inference optimization (e.g., distillation, quantization, pruning, serving optimization).
- Experience with high-performance computing and acceleration: CUDA and/or SIMD, profiling and performance tuning.
- Ability to read and reproduce key ideas from recent papers and implement algorithms with strong experimental discipline.
- Ability to communicate effectively in both Chinese (Mandarin) and English as the successful candidate will have to liaise with our counterparts in China.
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