Senior / Staff SLM & VLM Engineer - Post-Training, Tool Calling & Agents

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

1) 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/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

. 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.

5) 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.

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 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.