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

JABIL CIRCUIT (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

Jabil Circuit (Singapore) Pte. Ltd. is seeking a highly capable engineer/researcher to lead the R&D of Small Language Models and Vision-Language Models for low-latency production scenarios. You will be responsible for model training, optimization, and creating robust evaluation mechanisms.

The ideal candidate must possess strong software engineering abilities in Python and C++, as well as hands-on experience with model efficiency techniques. Fluency in both Chinese and English is essential for collaboration.

Qualifications

• Strong software engineering skills in Python and C++.
• Hands-on experience in model efficiency and inference optimization.
• Ability to communicate effectively in Chinese and English.

Responsibilities

• Lead R&D of Small Language Models and Vision-Language Models.
• Design and implement efficient compression strategies for SLM/VLM.
• Develop automated data collection and processing pipelines.
• Architect and implement a production-grade tool calling framework.

Skills

Software engineering skills in Python

Software engineering skills in C++

Model efficiency and inference optimization

High-performance computing

Communication skills in Mandarin

Communication skills 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

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 candidate will have to liaise with our counterparts in China.