Machine Learning Engineer

Wajo AI; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Wajo AI in San Francisco, CA is seeking a Machine Learning Engineer to design, implement, and deploy self-learning models and agents for real-world applications. You will work on-site with researchers and engineers to translate ideas into production systems, build data pipelines, run experiments, and monitor deployed services for reliability and safety.

You will collaborate across teams to optimize performance, document findings, and contribute to roadmap planning in a fast-paced research-driven

Qualifications

• Strong ML and DL fundamentals including RL, supervised and unsupervised learning.

Responsibilities

• Design, implement, and deploy self-learning models and agents for real-world applications.
• Develop and optimize ML algorithms and data pipelines.
• Run experiments, evaluate model performance, and iterate on architectures.
• Integrate models into scalable services and APIs.
• Monitor production systems and improve reliability, safety, and efficiency.
• Document work, participate in code reviews, and contribute to roadmap discussions.

Skills

Reinforcement learning

Supervised learning

Unsupervised learning

Python

PyTorch

TensorFlow

Data pipelines

Experimentation

Docker

Cloud platforms

Production deployment

Debugging

Cross-functional teamwork

Education

Bachelor's in CS

Advanced degree

Equivalent experience

Tools

Docker

Kubernetes

AWS

GCP

Job description

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.