Machine Learning Engineer

We are looking for a talented Machine Learning Engineer to join our team in revolutionizing the ION Factory OS for next-generation hardware creators. Based in our vibrant Downtown Los Angeles office, you will play a crucial role in our dynamic data team, supporting companies working on innovative projects in the fields of eVTOLs, rockets, robots, and autonomous vehicles. As a Machine Learning Engineer, you will be responsible for developing, implementing, and deploying machine learning models in production environments, collaborating with cross-functional teams, and continuously improving the performance of ML models. Strong programming skills, experience with machine learning algorithms and libraries, and knowledge of software engineering best practices are essential for this role.

Develop, implement, and deploy machine learning models in production environments

Work with cross-functional teams to define and prioritize ML use cases based on business requirements

Collaborate with data engineers, data scientists, and software engineers to build scalable and efficient ML pipelines

Implement ML workflows and monitoring solutions using MLflow or other similar life-cycle model management tools

Continuously improve and optimize the performance of ML models in production

Document and communicate ML processes, models, and results to stakeholders

• 3+ years of experience in machine learning engineering with a focus on production deployment
• Strong programming skills and experience in Python, Rust, or R
• Solid understanding of machine learning algorithms, such as supervised and unsupervised learning, reinforcement learning, and deep learning, as well as experience with popular machine learning libraries such as TensorFlow, PyTorch, or scikit-learn
• Hands-on experience with ML workflow management tools such as MLflow
• Experience with building, testing, and deploying ML models in production
• Solid understanding of data structures, algorithms, and statistics
• Strong knowledge of software engineering best practices and version control systems such as Git
• Experience with distributed computing technologies such as Apache Spark or Dask
• Familiarity with containerization technologies such as Docker and Kubernetes
• Knowledge of cloud platforms such as AWS, GCP, or Azure
• Experience with data engineering, data modeling, and data architecture
• Strong communication and collaboration skills
• Utilize infrastructure tooling, such as Terraform or Helm, to manage and automate the deployment, scaling, and monitoring of machine learning models in production environments

• Health Insurance; medical, vision, dental, & life insurance
• Paid Parental Leave
• Employee Stock Option Plan
• Team outings, group lunches, an open office, happy hours
• Paid holidays, sick days
• Flexible Fridays and PTO
• 401K