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) foredge / low-latency and cost-efficient production scenarios. You will own the continuous pretraining, supervised instruction tuning(SFT), andcompression/distillation pipelines, 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 pretraining andSFT for 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 compression strategies for SLM/VLM, includingknowledge distillation, pruning, and quantization-oriented training or post-training optimization.
Optimizemodel serving and inference forlow-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
Applypost-training methods such asPPO / DPO / GRPO-like optimization and reward modeling to align the model towardobjectivesincluding:
semantic understanding
tool-use success rate
content generation quality and consistency
Support bothoffline andonline iteration 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 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 fromrecent papers and implement algorithms with strong experimental discipline.
Ability to communicate effectively in both Chinese ( Mandarin)and English as the successful person will have to liaise with the our counterparts in China.
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.