Principal Software Engineer, AI & Data Platform

Jobtailor; Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

Jobtailor is seeking a senior data/AI engineer to architect and optimize data foundations for large-scale scientific and engineering outputs. You will shape data models, pipelines, and tooling to enable interactive analysis, automation, and model training across distributed environments.

The role emphasizes end-to-end delivery of production-grade data systems, scalable processing patterns, and robust ML data workflows, with leadership in setting engineering standards and best practices.

Qualifications

• Bachelor’s or Master’s degree in CS or related engineering field.
• 10+ years building and shipping production software.
• Expert Python with end-to-end shipping experience.
• Experience with large-scale data systems, including object storage, analytics, and training-optimized formats.
• Experience training or fine-tuning models for structured outputs, tool use, or domain-specific applications.
• Strong understanding of relational, document, and columnar data models.
• Cloud, enterprise, and distributed compute environments experience.
• Ability to set technical direction in ambiguous early-stage environments.

Responsibilities

• Architect the data foundation for large-scale scientific and engineering output, ensuring results are clean, queryable, and reusable.
• Model domain-specific scientific data to support interactive analysis, automation, and ML workflows.
• Build scalable data processing patterns across object storage, analytical stores, and training-optimized formats.
• Create ML data pipelines for curation, deduplication, formatting, evaluation sets, and regression tracking.
• Develop training and fine-tuning pipelines for models used in scientific products.
• Design intelligent workflow interfaces linking user intent, platform capabilities, and executable workflows.
• Own model evaluation, benchmarking, and automated scoring with measurable iterations.
• Set data and AI engineering standards and turn them into code, docs, and reusable patterns.

Skills

Python

Data pipelines

Machine learning

Cloud

Production software

Education

Bachelor’s or Master’s degree in CS or related engineering field

Tools

SQL

NoSQL

Docker

Kubernetes

Job description

Responsibilities

• Architect the data foundation for large scale scientific and engineering output, keeping results clean, queryable, reusable, and ready for model training.
• Model domain specific scientific data so the same datasets can support interactive analysis, automation, and downstream machine learning workflows.
• Build scalable data processing patterns across object storage, analytical stores, and training optimized formats.
• Create machine learning data pipelines for curation, deduplication, formatting, evaluation sets, and regression tracking.
• Build and operate training and fine tuning pipelines for models used in scientific and workflow driven products.
• Develop intelligent workflow interfaces that connect user intent, structured platform capabilities and executable workflows without exposing unnecessary complexity to users.
• Own model evaluation, benchmarking, automated scoring, and quality tracking so each iteration is measurable.
• Set data and AI engineering standards for the team and turn them into code, documentation, and reusable patterns.

Requirements

• Bachelor’s or Master’s degree in Computer Science or a related engineering field, with 10 plus years building and shipping production software.
• Expert Python and a strong record of shipping systems end to end.
• Deep experience with large scale data systems, including object storage, analytical processing, training optimized formats, and production data pipelines.
• Hands on experience building data pipelines for model training, fine tuning, evaluation, and continuous improvement.
• Direct experience training or fine tuning models for structured outputs, tool use, workflow automation, or domain specific applications.
• Strong understanding of relational, document, and columnar data models, with judgment about where each belongs.
• Comfort operating in cloud, enterprise, and technical compute environments, including distributed training or large scale batch processing.
• Ability to set technical direction in ambiguous early stage environments and carry it through implementation.
• Nice to have: Experience applying machine learning to scientific data, such as property prediction, generative models, graph based methods, or simulation data.
• Experience with atomistic, materials, chemistry, or engineering data systems.
• Experience with retrieval over structured data, knowledge graphs, or hybrid search systems.
• Experience designing APIs or tool interfaces that intelligent systems can call reliably.
• Experience building complex data and machine learning workflows on production orchestrators.
• Contributions to open source machine learning, data infrastructure, or scientific computing tools.

Core Competencies

Demonstrates expertise in building and optimizing data pipelines for machine learning and scientific applications, with a strong focus on data quality, model training, and workflow automation. Proficient in Python and experienced in large scale data systems, ensuring effective data management and analysis.