Lead Generative AI Engineer
at Tykhe Inc (pronounced Tie-key)
in
Palo Alto,
California
Posted in Other 12 days ago.
Type: full-time
Job Description:
Job Description:
We are looking for an experienced 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.
Key Responsibilities:
1. Architect and refine foundation model infrastructure to support the deployment of optimized AI
models with a focus on C/C++, CUDA, and kernel-level programming enhancements.
2. Implement state-of-the-art optimization techniques, including quantization, distillation, sparsity,
streaming, and caching, for model performance enhancements.
3. Spearhead the development of Vision pipelines, ensuring scalable training and inference workflows of
10s and 100s of billions of parameter foundation models.
4. Should be able to innovate for the state-of-the-art architectures involving Panoptic Segmentation,
Image Classification and Image Generation. It is expected that the candidate experiments with the
internals of Vision Transformers and convolutional Models like ConvNext, CLIP, Visual Question
Answering (VQA) and Diffusion Models. Practice around AI Arts, Image Prompts, Conditional Image
Generation will be an additional advantage.
5. Execute training and inference processes with a key emphasis on minimizing latency and maximizing
throughput, utilizing GPU clusters and custom hardware.
6. Innovate on current model deployment platforms, employing AWS, GCP, and GPU clusters, to enable
high scalability and responsiveness.
7. Integrate and tailor frameworks such as PyTorch, TensorFlow, DeepSpeed, and FSDP for the
advancement of super-fast model training and inference.
8. Advance the deployment infrastructure with MLOps frameworks such as KubeFlow, MosaicML,
Anyscale, Terraform, ensuring robust development and deployment cycles.
9. Enhance post-deployment mechanisms with exhaustive testing, real-time monitoring, and
sophisticated explainability and robustness checks.
10. Drive continuous improvement initiatives for deployed models with automated pipelines for drift
detection and performance degradation.
11. Lead the charge in model management, encompassing version control, reproducibility, and lineage
tracking.
12. Cultivate a culture of high-performance computing and optimization within the AI/ML domain,
propagating best practices and knowledge sharing.
Qualifications:
1. Ph.D. with 5+ years or MS with 8+ years of experience in ML Engineering, Data Science, or related
fields.
2. Demonstrated expertise in high-performance computing with proficiency in Python, C/C++, CUDA, and
kernel-level programming for AI applications.
3. Extensive experience in the optimization of training and inference for large-scale AI models, including
practical knowledge of quantization, distillation, and Vision Pipelines.
4. It will be of additional benefit if the Candidate understands Diffusion Models (DDPM), Variational
Autoencoders, Bayesian Modelling, Stochastic Variational Inference (SVI) and Reinforcement
Learning.
5. Experience in building 10s and 100s of billions of parameters generative AI foundation models
6. AI training job scheduling, orchestration, and management via SLURM and Kubeflow.
7. Proven success in deploying optimized ML systems on a large scale, utilizing cloud infrastructures and
GPU resources.
8. In-depth understanding and hands-on experience with advanced model optimization frameworks such
as DeepSpeed, FSDP, PyTorch, TensorFlow, and corresponding MLOps tools.
9. Familiarity with contemporary MLOps frameworks like MosaicML, Anyscale, Terraform, and their
application in production environments.
10. Strong grasp of state-of-the-art ML infrastructures, deployment strategies, and optimization
methodologies.
11. An innovative problem-solver with strategic acumen and a collaborative mindset.
12. Exceptional communication and team collaboration skills, with an ability to lead and inspire.