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

Jabil; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

Jabil in Singapore is seeking a capable engineer/researcher to lead the R&D of Small Language Models and Vision-Language Models for edge production scenarios. You will manage pretraining and optimization efforts, while collaborating with teams to enhance model efficiency and quality.

Ideal candidates possess strong Python and C++ skills, experience with model optimization, and effective communication in both Chinese and English. Excellent problem-solving skills and a hands-on approach are essential.

Qualifications

• Experience building ML training/evaluation pipelines in PyTorch.
• Hands-on experience in model efficiency and inference optimization.
• Ability to read recent research papers and implement algorithms.

Responsibilities

• Lead R&D of Small Language Models and Vision-Language Models.
• Conduct continuous pretraining and instruction tuning.
• Design efficient compression strategies for models.

Skills

Software engineering skills in Python

Software engineering skills in C++

Model efficiency and inference optimization

Data pipeline automation

Communication in Chinese

Communication in English

Tools

PyTorch

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

• 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.
• 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.
• 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
• 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.
• 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.
• 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.