Frost & Sullivan MetaBrain Early Careers Program – AI Model Training & Evaluation Internship

Frost & Sullivan; Singapore; Remote; SGD 13,000 - 20,000; Part time

Benefits offered by this job

Paid internship stipend

Job summary

Frost & Sullivan invites postgraduate students, recent graduates, and early-career professionals to join our MetaBrain internship programs. Interns will work on applying AI/ML to business research, with a focus on turning research into reusable software-enabled services.

Experience in research, language models, and data-driven evaluation is valued. Research and advisory workflows will be enhanced through controlled experiments, evaluation datasets, and collaboration with industry researchers,

Qualifications

• Master's/PhD in AI/ML with substantial training.
• Proficient in Python and at least one ML framework.
• Ability to communicate results to non-technical colleagues.

Responsibilities

• Create reliable evaluation assets with annotation guidance and provenance.
• Run controlled experiments using Python pipelines and comparisons of configurations.
• Connect metrics to business quality: groundedness, numerical accuracy and relevance.
• Deliver safe improvement cycles with experiment logs, regression tests and documentation.

Skills

Python

ML frameworks

Data analysis

Education

Master's/PhD in AI/ML or related field

Tools

Python

TensorFlow or PyTorch

Job description

Launch Your Career with Frost & Sullivan

At Frost & Sullivan, we believe that the future belongs to curious minds, innovative thinkers, and problem-solvers who are eager to make an impact. We are inviting applications from postgraduate students, recent graduates, and early-career professionals with up to two years of experience to join our growing global teams across various business, technology, research, consulting, AI, data, and corporate functions.

The Opportunity

Frost & Sullivan is looking for intern roles supporting MetaBrain. The work combines applied AI, business understanding, structured knowledge, quantitative models and trustworthy engineering to transform research and advisory into reusable software-enabled services. Build more than a demonstration. Work with industry researchers, advisors and engineers to turn AI capability into tested decision‑intelligence software that enterprises can use.

Role Overview

Show how your technical work supports a business problem. Research, consulting or advisory experience is preferred but not mandatory. Academic projects, thesis and reproducible research implementations are valid evidence; internships do not require prior full‑time employment.

Build measurable improvements to MetaBrain's research and advisory workflows. Translate domain‑expert feedback into evaluation datasets, experiment designs and controlled changes to retrieval, model configuration or approved model training.

Proposed engagement

Stipend: Yes, paid internship.

Duration: Preferably six months, possibility of an extension up to 12 months based on performance and where academic arrangements and work authorization permits are in place.

Full‑time Conversion: Depends on assessed performance, a suitable vacancy and eligibility; it is not guaranteed.

Essential Requirements

Master's/PhD study or qualification with substantial AI/ML training. Python and practical experience with at least one ML framework; knowledge of learning objectives, overfitting, dataset splits and evaluation. A project involving language models, natural‑language processing, retrieval, model adaptation or rigorous ML evaluation. Ability to communicate results and uncertainty to non‑technical colleagues.

Preferred: Parameter‑efficient adaptation, preference data, information retrieval, synthetic‑data evaluation, annotation‑quality measurement or experiment tracking. Business research, knowledge management or advisory exposure is desirable. For business‑heavy work, strong analytical writing and numerical validation are especially valuable.

Key responsibilities

• Create reliable evaluation assets: Work with advisors on annotation guidance, reference answers and domain‑specific tests. Preserve evidence and provenance; separate training, validation and held‑out data; manage versions and detect duplication or contamination.
• Run controlled experiments: Implement Python pipelines to compare model/prompt configurations, retrieval choices and domain‑adaptation techniques. Use transparent baselines, ablation studies and repeatable settings rather than selecting only favorable examples.
• Connect metrics to business quality: Measure groundedness, numerical accuracy, relevance, appropriate abstention and task success. Calibrate automated grading against expert review; categorize errors and recommend improvements with cost and latency trade‑offs.
• Deliver safe improvement cycles: Maintain experiment logs, regression tests, model/dataset documentation and rollback records. Support fine‑tuning or preference‑based learning only where data rights, expected benefits and platform approvals justify the work.