Remote Senior AI Engineer: Training & Evaluation

prolificacademicltd

Stati Uniti

Remoto

USD 91.000 - 129.000

Tempo pieno

14 giorni+

Aumenta le possibilità di ricevere una risposta da questo datore di lavoro

Il tuo curriculum e una lettera di presentazione personalizzati, che corrispondono esattamente a ciò che sta cercando.

Supera i filtri ATS

Vantaggi offerti da questo lavoro

Fully remote
Flexible hours
Short onboarding

Descrizione del lavoro

Prolific Academic Ltd in the United States seeks senior AI/ML engineers based in Jacksonville for a paid participant role in LLM training and evaluation. Engagement is task-based, with compensation tied to hours contributed, not a traditional job.

Responsibilities include assessing explanations of neural architectures, validating ML code and notebooks, and supplying structured feedback for RLHF. Fully remote, flexible hours, with many tasks fitting within about one hour of work.

Competenze

  • BS/MS/PhD in CS/AI/Robotics or related quantitative field with ML focus.
  • Experience building, deploying, or fine-tuning ML models in production.
  • Knowledge of Transformers, CNNs, RNNs and optimization methods.
  • Hands-on prompt engineering, RLHF or retrieval-augmented generation workflows.
  • Ability to audit model logic and detect data contamination.

Mansioni

  • Assess AI explanations of architectures, loss functions, and backpropagation for technical accuracy.
  • Validate ML code and notebooks, including training loops and preprocessing scripts.
  • Provide structured human feedback powering RLHF pipelines.
  • Trace how models handle chain-of-thought prompts and identify breakdowns.
  • Run benchmarks comparing outputs against defined metrics.

Conoscenze

ML model eval
Prompt engineering
RLHF
RAG workflows
Transformers
PyTorch
TensorFlow
Keras

Formazione

CS/AI/Robotics degree

Strumenti

PyTorch
TensorFlow
Keras
NumPy
Pandas
LangChain

Descrizione del lavoro

Prolific Academic Ltd in the United States seeks senior AI/ML engineers based in Jacksonville for a paid participant role in LLM training and evaluation. Engagement is task-based, with compensation tied to hours contributed, not a traditional job.

Responsibilities include assessing explanations of neural architectures, validating ML code and notebooks, and supplying structured feedback for RLHF. Fully remote, flexible hours, with many tasks fitting within about one hour of work.

Ottieni la revisione del curriculum gratis e riservata.

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