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 English and Mandarin as this role will need to liaise with Mandarin speaking counterparts/stakeholders.