MLOps and Data Engineer

As an ML Ops and Data Engineer at our Machina Labs, you will play a pivotal role in the integration of machine learning models, data pipelines, and operational processes to optimize the performance and functionality of our robotic systems. You will collaborate closely with our software development, machine learning, and robotics teams to ensure seamless maintenance of our data pipeline and deployment of AI systems.

• Machine Learning Deployment and Integration:
• Collaborate with machine learning engineers to operationalize and deploy machine learning models onto robotic systems.
• Design and implement robust APIs and interfaces for communication between robotics hardware and deployed models.
• Develop strategies for model monitoring, versioning, and updates to ensure ongoing reliability and performance.

• Design, implement, and manage end-to-end data pipelines that collect, process, and store sensor data from robotic systems.
• Ensure data quality, consistency, and availability for both real-time and historical analysis.
• Implement data transformation, feature engineering and enrichment techniques to prepare data for training and validation.

• Implement monitoring solutions to track the health and performance of deployed robotic systems and associated machine learning models.
• Analyze performance metrics and proactively identify opportunities for optimization and improvement.
• Collaborate with cross-functional teams to iteratively enhance system efficiency and response times.

• Collaborate closely with software developers, machine learning engineers, and robotics team to ensure alignment on technical requirements and objectives.
• Maintain comprehensive documentation for deployed models, data pipelines, and system architecture.

• Bachelor's, Master's, or PhD degree in Computer Science, Software Engineering, or related field.
• 3+ years of experience in data engineering and/or ML Ops
• Strong proficiency in programming languages such as Python, Scala, SQL, along with experience with popular data storage systems (e.g. Data Lakes)
• Proven experience in deploying machine learning models in production environments.
• In-depth knowledge of data engineering principles, ETL processes, and data storage solutions.
• Familiarity with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes).
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and related services.
• Strong problem-solving skills and ability to work in a collaborative, fast-paced environment.

$100,000 - $166,000 a year

Salary + competitive equity package. Salary range varies based on level of experience.