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

At Jabil (NYSE: JBL), we are proud to be a trusted partner for the world's top brands, offering comprehensive engineering, supply chain, and manufacturing solutions. With 60 years of experience across industries and a vast network of over 100 sites worldwide, Jabil combines global reach with local expertise to deliver both scalable and customized solutions. Our commitment extends beyond business success as we strive to build sustainable processes that minimize environmental impact and foster vibrant and diverse communities around the globe.

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.

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