SLM & VLM Engineer — Post-Training, Tool Calling & Agents

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 SIMD,profilingand 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.