Role overview
- Design, implement, and optimize state-of-the-art machine learning models and training architectures.
- Build and scale data pipelines for model pretraining, fine-tuning, and evaluation.
- Develop and maintain reinforcement learning and evaluation environments that assess model reliability and robustness.
- Conduct advanced model analysis to identify behavioral failure modes and performance limitations.
- Rapidly iterate on models, datasets, and evaluation frameworks with minimal supervision.
- Integrate new research insights and experimental findings into applied systems.
- Contribute to technical documentation and reproducible workflows that meet high research standards.
What we're looking for
- Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field (required)
- Demonstrated expertise in training, evaluating, and deploying advanced ML models
- Strong background in multimodal learning , representation learning , or reinforcement learning
- Fluency in Python and proficiency with PyTorch , TensorFlow , or equivalent ML frameworks
- Experience with data preprocessing , feature engineering , and scalable ML pipelines
- Deep understanding of AI model evaluation, interpretability, and bias analysis
- Self-directed, reliable, and detail-oriented with a high standard for research quality
- Excellent written and verbal communication skills
- Location: Remote
- Type: Contractor
- Time Commitment: 40 hours per week, with at least 3 hours overlapping PST (9am–5pm)
- Process: Includes a take-home technical assessment (approx. one-week turnaround).
Tags & focus areas
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