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

Jabil; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Jabil in Singapore seeks a highly capable engineer/researcher to lead the R&D of Small Language Models (SLMs) and Vision-Language Models (VLMs) for edge, low-latency production scenarios. You will own continuous pretraining, instruction tuning (SFT), and compression/distillation pipelines, collaborating with platform teams to deliver reliable gains in inference efficiency and model quality.

Experience with PyTorch, HPC, and tool-call systems is expected; able to communicate in English and

Qualifications

• Strong software eng in Python and C++, with PyTorch training/eval pipelines
• Hands-on model efficiency and inference optimization experience
• Proficient in HPC concepts (CUDA/SIMD) and performance tuning
• Ability to read/reproduce ideas from recent papers and implement methods
• Communication in English and Mandarin with cross-border teams

Responsibilities

• Lead SLM/VLM training and continuous pretraining workflows
• Design compression, distillation, and edge inference optimizations
• Build and maintain a production-grade tool calling framework
• Apply RL/reward modeling for alignment and reliability
• Automate data collection, cleaning, labeling, and dataset versioning
• Rigorous offline/online evaluation and rapid iteration

Skills

Python

C++

PyTorch

ML pipelines

Model optimization

CUDA

Mandarin

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.

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 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.

Accessibility Accommodation

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