Founding AI Scientist

PROVENA PTE. LTD.; Singapore; On-site; SGD 120,000 - 170,000; Full time

Job summary

PROVENA PTE. LTD. in Singapore seeks a founding research engineer to own data curation across all training stages. You’ll work with the founders to turn research into feature-rich data infrastructure that underpins the company’s AI training pipeline.

You will measure data quality and coverage, diagnose data-related model issues, and ship research into production through experiments and reports.

Qualifications

• Strong machine learning and deep-learning fundamentals.
• Hands-on experience with ML experimentation and production prototypes.
• Comfort reading ML research and implementing promising ideas.

Responsibilities

• Own data curation across pre-training to post-training stages.
• Turn research into product features and maintain data infrastructure.
• Design data interventions (pruning, filtering, augmentation) and validate gains.

Skills

ML fundamentals

Deep learning

LLM training

Research independence

Data curation

Tools

PyTorch

JAX

Job description

Obsessed about training data? We're building the data infrastructure layer for AI training.

The role

As our founding research engineer, you'll own data curation as a measured discipline across every stage of training, from pre-training to post-training. Working alongside the founders, you'll turn what works into product features — and as one of the first researchers, the methods and data infrastructure you build become the foundation the company runs on.

Examples of work you might do

• Measure what's in a dataset — quality, provenance, safety, and coverage metrics that predict downstream performance
• Diagnose where data will hurt a model — decontamination, difficulty annotation, multilingual asymmetries, long-tail gaps
• Design data curation interventions — pruning, filtering, synthetic augmentation, relabeling — and attribute the gains to specific changes
• Turn the latest research into running experiments — training and evaluation loops that prove a data hypothesis
• Ship the methods that work as product features, and share findings as technical reports or papers

What we’re looking for

• Data obsession — you want to know exactly which properties of a dataset move a model, and you won't trust a result you can't measure
• Bias toward iteration — you design the smallest experiment that answers the question and move fast on the answer
• Research that ships — you optimize for models that measurably improve, not for leaderboard wins
• Product engineer — you own problems end to end and turn research into features that ship, not just results in a notebook
• Drawn to data-centric AI — you're excited by data-centric methods and the move toward autonomous AI research

Required qualifications

• Strong machine learning and deep-learning fundamentals
• Enough software engineering and PyTorch / Jax experience (or willingness to learn) to run ML experiments and build production prototypes
• Hands-on experience in one or more stages of training and evaluating LLMs/vLLMs
• Industry or research experience in one or more of: data curation, data pruning & selection, synthetic data generation, curriculum learning, dataset distillation, large-scale language/multimodal training
• Comfortable reading ML research — sourcing, vetting, and implementing promising ideas from the literature
• Able to drive applied research independently in a fast, ambiguous, early-stage environment

Nice to have

• Post-training experience — SFT, preference optimization (DPO, RLVR), or reward modeling
• Multilingual or multimodal data work
• Multi-GPU / distributed training experience
• Open-source, HuggingFace contributions
• Public technical writing, published research papers