Job Summary

We are looking for a highly capable engineer/researcher to lead the R&D ofSmall Language Models(SLMs)andVision-Language Models(VLMs)foredge / low-latencyand cost-efficient production scenarios. You will own thecontinuous pretraining supervised instruction tuning(SFT) andcompression/distillationpipelines and work closely with platform teams to deliver reliable measurable improvements ininference efficiency tool-use success rate and overall model quality.

Key Responsibilities

1) SLM/VLM Training: Continuous Pretraining & Instruction Tuning (SFT)

• Conductcontinuous pretrainingandSFTfor SLMs and VLMs to improve task performance and domain adaptation.

• Build reproducible training workflows inPyTorch including data processing training evaluation and model versioning.

2) Compression Distillation & Edge/Low-Latency Inference Optimization

• Design and implementefficient compressionstrategies for SLM/VLM includingknowledge distillation pruning and quantization-oriented training or post-training optimization.

• Optimizemodel serving and inference forlow-latency / edgescenarios 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-gradetool 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

4) RL & Reward Modeling for Alignment and Tool-Use Reliability

• Applypost-trainingmethods such asPPO / DPO / GRPO-likeoptimization and reward modeling to align the model towardobjectivesincluding:

• semantic understanding

• tool-use success rate

• content generation quality and consistency

• Support bothofflineandonlineiteration loops including policy evaluation regression checks and safe deployment gating.

5) Data Pipeline Automation (Collection Cleaning Curation)

• Design automated pipelines fordata 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.

6) 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 inPython and C including experience building ML training/evaluation pipelines inPyTorch.

• Hands-on experience inmodel efficiency and inference optimization(e.g. distillation quantization pruning serving optimization).

• Experience with high-performance computing and acceleration:CUDA and/or SIMDprofilingand performance tuning.

• Ability to read and reproduce key ideas fromrecent papersand implement algorithms with strong experimental discipline.

• Ability to communicate effectively inboth Chinese (Mandarin)and English as the successful person will have to liaise with the our counterparts in China.