ML/AI fundamentals - fine-tuning (SFT/preference), prompting, model-based eval, and the failure modes of each.
At least one year of hands-on work in the field. Multimodal dataset experience - building image/video/audio/text datasets to train or evaluate models.
Strong Python + ML tooling - PyTorch (or equivalent), HF ecosystem, comfort running training/sampling jobs on real infra.
Web/UI engineering - production React/TypeScript; design, vibe code and ship clean, usable interfaces for annotation and internal tools.
Requirements:
Design human and model-based datasets for training and evaluating MLLMs (image/video/audio/text data).
Run frontier model evaluations and implement common metrics.
Use MLLMs to bootstrap, augment, and rebalance datasets.
Fine-tune models on labeled datasets.
Job description
Our Client, an Internet Content and Information company, is looking for a Software Engineer III for their Remote location.
Responsibilities:
Design human and model-based datasets for training and evaluating MLLMs (image/video/audio/text data).
Run frontier model evaluations and implement common metrics.
Use MLLMs to bootstrap, augment, and rebalance datasets.
Fine-tune models on labeled datasets.
Ship annotation task UIs and data-heavy internal tools (React/TypeScript)
Run secure ingestion pipelines from warehouse/warm-storage/flat files at scale.
Meeting deadlines, executing tasks with accuracy, etc.
Requirements:
Must-Have Skills:
ML/AI fundamentals - fine-tuning (SFT/preference), prompting, model-based eval, and the failure modes of each.
At least one year of hands-on work in the field. Multimodal dataset experience - building image/video/audio/text datasets to train or evaluate models.
Strong Python + ML tooling - PyTorch (or equivalent), HF ecosystem, comfort running training/sampling jobs on real infra.
Web/UI engineering - production React/TypeScript; design, vibe code and ship clean, usable interfaces for annotation and internal tools.
Data pipelines - SQL plus reliable large-scale data movement: batching, idempotent dedupe, parallelism, retries.
Experience in mid sized tech, preference for (Google, Apple, client, or other FAANG)
Nice-to-have Skills:
MS or PhD in AI or related field.
Research track record in MLLMs - demonstrated through publications or impactful contributions to major projects.
Open-source contributions - experience shipping and maintaining public-facing repositories.
Years of overall experience required:
5-10+ years of hands-on experience in building multimodal datasets, implementing machine learning evaluations, and developing production-level data and annotation tools.
Degrees/certifications required: Academic background in Computer Science, Engine
