Principal Software Engineer, AI & Data Platform (Xora Portfolio Company)

About Elemynt

Elemynt builds secure AI infrastructure for scientific and engineering R&D teams. Our platform helps organizations connect data, models, compute, and expert workflows in environments where reliability, traceability, and data control matter.

We are building a small, ambitious engineering team across Singapore and the United States to turn advanced scientific computing into production software that real technical teams can use.

About The Role

Elemynt's platform turns scientific and engineering data into reusable assets for analysis, model training, and automated workflows.

This role owns the data and AI engineering foundation that makes those systems reliable, scalable, and measurable. You will define core patterns for data modeling, training pipelines, evaluation systems, and intelligent workflow interfaces, then prove those patterns in production code.

This is a hands on principal role for someone who can set technical direction and still build the hardest parts themselves.

What You Will Do

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

What We Are Looking For

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

LOCATION

Singapore or United States. Work model is on site or hybrid, depending on location.

CLOSING NOTE

You do not need to tick every box. If this is clearly your kind of work, we would like to hear from you.