What are the responsibilities and job description for the Lead Generative AI Research Engineer position at Tykhe Inc?
Would you be interested to be a part of fast-growing AI company in Palo Alto, California where you contribute your experience as a Lead Generative AI Engineer to train, optimize, scale, and deploy a variety of generative AI models such as large language models, voice / speech foundation models, vision and multi-modal foundation models using cutting-edge techniques and frameworks.
In this hands-on role, you will architect and implement state of art neural architecture, robust training and inference infrastructure to efficiently take complex models with billions of parameters to production while optimizing for low latency, high throughput, and cost efficiency.
Qualifications :
- Ph.D. with 5 years or MS with 8 years of experience in ML Engineering, Data Science, or related fields.
- Demonstrated expertise in high-performance computing with proficiency in Python, C / C , CUDA, and kernel-level programming for AI applications.
- Extensive experience in the optimization of training and inference for large-scale AI models, including practical knowledge of quantization, distillation, and Vision Pipelines.
- It will be of additional benefit if the Candidate understands Diffusion Models (DDPM), Variational Autoencoders, Bayesian Modelling, Stochastic Variational Inference (SVI) and Reinforcement Learning.
- Experience in building 10s and 100s of billions of parameters generative AI foundation models
- AI training job scheduling, orchestration, and management via SLURM and Kubeflow.
- Proven success in deploying optimized ML systems on a large scale, utilizing cloud infrastructures and GPU resources.
- In-depth understanding and hands-on experience with advanced model optimization frameworks such as DeepSpeed, FSDP, PyTorch, TensorFlow, and corresponding MLOps tools.
- Familiarity with contemporary MLOps frameworks like MosaicML, Anyscale, Terraform, and their application in production environments.
- Strong grasp of state-of-the-art ML infrastructures, deployment strategies, and optimization methodologies.
- An innovative problem-solver with strategic acumen and a collaborative mindset.
- Exceptional communication and team collaboration skills, with an ability to lead and inspire.
