Data Scientist III

Jabil Circuit (Singapore) Pte Ltd; Singapore; On-site; SGD 140,000 - 230,000; Full time

Job summary

Jabil Circuit (Singapore) Pte Ltd is seeking a highly capable engineer/researcher to lead R&D of Small Language Models and Vision-Language Models for edge and low-latency production scenarios. You will own continuous pretraining, SFT, and compression/distillation workflows, delivering measurable improvements in inference efficiency and model quality.

You will design and implement a production-grade tool calling framework, optimize for low latency, and apply reward modeling to align outcomes with

Qualifications

• Strong software engineering in Python and C++, with ML training/evaluation pipelines in PyTorch.
• Hands-on experience in model efficiency and inference optimization (distillation, quantization, pruning, serving optimization).
• Experience with high-performance computing and acceleration: CUDA and/or SIMD, profiling and tuning.
• Ability to read/reproduce ideas from recent papers and implement algorithms with experimental discipline.
• Ability to communicate effectively in English and Mandarin with counterparts in China.

Responsibilities

• SLM/VLM Training: Continuous pretraining and instruction tuning (SFT) for SLMs and VLMs.
• Build reproducible training workflows in PyTorch, including data processing, training, evaluation and versioning.
• Compression, distillation & edge/low-latency inference optimization, including quantization and caching strategies.
• Design tool calling framework: cataloging, routing, validation, fallbacks, and observability.
• Apply RL & reward modeling techniques (PPO/DPO/GRPO-like) for alignment and tool-use reliability.
• Design automated data pipelines for collection, cleaning, curation, labeling and dataset versioning.
• Establish rigorous evaluation suites: offline benchmarks, A/B tests, regression checks.

Skills

Python

C++

PyTorch

CUDA

English & Mandarin

Model optimization

Tools

ML pipelines

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.