Wajo Ai

Company Description Wajo AI focuses on building self-learning agents that operate in real-world environments, solving complex problems with minimal human intervention. The company is driven by research and engineering that bridge cutting-edge machine learning with practical, scalable applications. Team members collaborate across disciplines to design systems that adapt to changing conditions and continuously improve over time. Wajo AI offers an environment where experimentation, rigorous testing, and responsible deployment are central to how products are created and delivered.
Role Description As a Machine Learning Engineer at Wajo AI, you will design, implement, and deploy self-learning models and agents for real-world applications. You will work full time on-site in San Francisco, CA, collaborating with research scientists, software engineers, and product teams to translate experimental ideas into production-ready systems. Day-to-day responsibilities include developing and optimizing machine learning algorithms, building data pipelines, running experiments, evaluating model performance, and iterating on model architectures. You will help integrate models into scalable services, monitor systems in production, and contribute to improving reliability, safety, and efficiency of deployed agents. The role also involves documenting work clearly, participating in code reviews, and contributing to technical discussions and roadmap planning.
Qualifications

• Candidates should possess strong skills in core machine learning and deep learning, including experience with algorithms for reinforcement learning, supervised and unsupervised learning.
• Candidates should possess strong programming and software engineering skills, including proficiency in Python and familiarity with ML frameworks such as PyTorch or TensorFlow.
• Candidates should possess skills in data handling and experimentation, including building data pipelines, working with large datasets, and designing rigorous evaluation and benchmarking processes.
• Candidates should possess skills in systems design and deployment, including experience with cloud platforms, containerization (e.g., Docker), and deploying models into production services or APIs.
• Candidates should possess strong analytical and problem-solving skills, with the ability to reason about complex systems, debug issues, and optimize performance.
• Relevant qualifications include a bachelor’s or advanced degree in Computer Science, Electrical Engineering, Mathematics, or a related field, or equivalent practical experience.
• Beneficial experience includes prior work on real-world autonomous systems or agents, applied reinforcement learning, robotics, simulation environments, or safety-aware ML deployment.
• Effective communication skills, ability to work collaboratively in cross-functional teams, and a willingness to learn and adapt to new tools and methods are important for success in this role.