Job Description

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.

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-latencyand cost-efficient production scenarios. You will own the continuous pretraining, supervised instruction tuning(SFT), and compression/distillationpipelines, 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 pretrainingand SFTfor 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 compressionstrategies for SLM/VLM, including knowledge distillation, pruning, and quantization-oriented training or post-training optimization.

• Optimizemodel serving and inference for low-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-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-trainingmethods such as PPO / DPO / GRPO-likeoptimization and reward modeling to align the model towardobjectivesincluding:

• semantic understanding

• tool-use success rate

• content generation quality and consistency

• Support both offlineand onlineiteration 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,profilingand performance tuning

• Ability to read and reproduce key ideas from recent papersand 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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Jabil, including its subsidiaries, is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, age, disability, genetic information, veteran status, or any other characteristic protected by law.

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