AI Research Scientist

You will design, train, evaluate, and optimize machine learning models, including large language models, multimodal architectures, transformers, and diffusion models. You will research model efficiency, quantization, compression, and on-device deployment; prototype architectures, training methods, and inference strategies; and build benchmarks, datasets, and experimental frameworks. You will analyze and communicate results, document research, present technical work, identify emerging technologies, and collaborate with engineering teams to integrate research findings into production systems.

Responsibilities

• Design, train, and optimize machine learning models, including LLMs, multimodal models, transformers, and diffusion architectures.
• Conduct research on model efficiency, quantization, compression, and on-device deployment.
• Prototype model architectures, training methods, and inference strategies for distributed AI.
• Develop and evaluate benchmarks, datasets, and experimental frameworks to test model performance.
• Collaborate with engineering teams to integrate research findings into production systems.
• Stay current on research in deep learning, generative AI, and distributed machine learning.
• Analyze experimental results and communicate insights to technical and non-technical stakeholders.
• Document research findings, contribute to internal papers, and present technical work across the organization.
• Identify emerging technologies and propose research directions aligned with strategic priorities.

Requirements

• 4+ years of experience, including graduate research, in machine learning research, AI model development, or related fields.
• Expertise in deep learning architectures, including transformers, CNNs, RNNs, and diffusion models.
• Experience training and fine-tuning large-scale models.
• Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Experience building datasets, designing experiments, and validating machine learning model performance.
• Understanding of optimization techniques including quantization, distillation, pruning, and hardware-aware training.
• Problem-solving skills and ability to work independently on complex research tasks.
• Communication skills for presenting research findings to diverse audiences.
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
• Master’s or PhD in Machine Learning, Computer Science, AI, or a related field.
• Experience with distributed training, edge inference, or on-device machine learning.
• Research experience in generative AI, reinforcement learning, or multimodal learning.
• Familiarity with privacy-preserving machine learning techniques such as federated learning.
• Experience contributing to academic publications, patents, or open-source machine learning projects.

Benefits

• Comprehensive health, dental, and vision benefits package.
• 401(k) match.
• Equity options.
• $200/month Health & Wellness stipend.
• Continuing Education support.
• $500/year Function Health subscription.
• Free parking for in-office employees.
• Flexible Time Off (FTO).
• Parental leave for eligible employees.
• Supplemental life insurance.