Staff Machine Learning Engineer

webAI•Austin, TX

About The Position

webAI is the first end-to-end private AI platform. Enterprises and Governments use webAI to bring AI to their data, powering specialized intelligence trained on their own knowledge and compounding in value with every use. Our approach is guided by a simple philosophy: AI should be specialized, sovereign, efficient, and sustainable. It should solve real problems, not just burn more compute. Our technology enables the development of powerful AI using limited amounts of data, challenging the underlying assumption that big data is the key to unlocking the full power of AI. We are offering a superior career opportunity to join a dynamic and fast-growing team that fosters an exciting and growth-oriented work culture; an opportunity where you can truly put your name on meaningful change for an entire civilization. We are seeking an experienced Staff Machine Learning Engineer with a strong background in Large Language Models (LLMs) and/or Mixture of Experts (MoEs). The ideal candidate will have a proven track record of developing and deploying advanced AI models. You will lead core product initiatives across on-device inference optimization, quantization, RAG, agentic framework and/or tool calling. You will also be responsible for end-to-end delivery working cross functionally with other engineering functions and lead the sub-team.

Requirements

Advanced degree (Ph.D. preferred) in Computer Science, or a related field.
Proven track record of building and innovations through publications or industry experience.
Minimum of 6 years of experience in machine learning, with specific expertise in Large Language Models and Mixture of Experts.
Strong programming skills in Python and machine learning frameworks like TensorFlow and/or PyTorch.
Demonstrated ability to lead complex projects and work collaboratively in a team environment.
Excellent problem-solving skills and a passion for innovation.

Nice To Haves

Experience with cloud computing services (AWS, Azure, GCP).
Knowledge of Big Data technologies (Hadoop, Spark).
Familiarity with containerization and orchestration technologies (Docker, Kubernetes).
Publications or presentations in recognized Machine Learning journals or conferences.

Responsibilities

Lead the development and optimization of Large Language Models and Mixture of Experts models.
Collaborate with cross-functional teams to integrate ML models into our platform.
Conduct cutting-edge research in machine learning, with a focus on improving the performance and efficiency of LLMs.
Stay abreast of the latest advancements in AI and ML, and apply this knowledge to improve our models and methodologies.
Mentor junior engineers and contribute to the team’s knowledge sharing and best practices.