Staff Machine Learning Engineer

About Us:

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.

About the Role:

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.

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.

Qualifications:

• 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.

Preferred Skills:

• 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.

Benefits:

• Competitive salary and performance-based incentives
• Comprehensive health, dental, and vision benefits package
• Including a high-deductible HSA option with company contribution
• 401k match (US-based only)
• Flexible PTO Policy
• Parental leave for eligible employees
• Continuing education support
• Basic, company-paid life insurance
• Supplemental life insurance
• Company-paid short and long term disability
• Free parking for in-office employees

webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.