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

Jabil; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Jabil is seeking a highly capable engineer/researcher to lead the R&D of Small Language Models and Vision-Language Models for edge/low-latency 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.

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.

Responsibilities

• SLM/VLM Training: Continuous Pretraining & Instruction Tuning (SFT) and build reproducible training workflows in PyTorch.
• Compression, Distillation & Edge/Low-Latency Inference Optimization: implement efficient compression strategies and optimize throughput and cost-per-token.
• Tool Calling System: Architect and implement a production-grade tool calling framework with cataloging, routing, validation, and observability.
• RL & Reward Modeling for Alignment and Tool-Use Reliability: apply PPO/DPO/GRPO-like methods and reward modeling for alignment and reliability.
• Data Pipeline Automation: design automated pipelines for data collection, cleaning, labeling, and dataset version management.
• Rigorous Evaluation, Testing & Iteration: offline benchmarks, regression tests, and reliability metrics with A/B tests and ablations.

Skills

Python

C++

PyTorch

Model optimization

Distillation

Tools

CUDA

Job description

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 OptimizationDesign 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 & ObservabilityArchitect 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 ReliabilityApply 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 & IterationBuild 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.

Accessibility Accommodation

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