Senior Software Engineer, Machina Labs

• This role is ideal for someone who thrives at the intersection of theory and application—where cutting-edge research meets production-grade impact
• MS or PhD. in Data Science, Computer Science, Machine Learning, Statistics, or a related field
• 4+ years of hands-on experience in machine learning systems, algorithms, and applications (e.g. deep learning, time series analysis, etc.)
• Experienced and very comfortable coming up with machine learning architectures and training models from scratch
• Extensive Python programming experience
• Familiarity with big data platforms (Hadoop, Spark, Hive) and analytics environments (Databricks, Sagemaker/Azure ML, Jupyter)
• Experience in build/release systems and processes
• Quick learner of new technologies and experienced in fast-paced iterative design

Desirable

• As a Senior Machine Learning Engineer at Machina Labs, you won’t just be training models—you’ll be shaping the future of manufacturing. Every day, our robotic systems generate massive streams of sensor data, and your challenge will be to turn that raw information into intelligence that transforms how things are made
• From cloud-scale experimentation to edge-ready deployment, you’ll develop ML models that predict and optimize the critical parameters of our production process
• You’ll collaborate closely with roboticists, software engineers, and manufacturing experts to design algorithms that make our systems smarter, faster, and more reliable. Think of it as building the “brain” that powers the factory of the future
• Your work won’t sit in a paper or a sandbox; it will directly influence how aircraft, spacecraft, and next-generation products are built
• Identify opportunities for machine learning automation and predictive modeling by analyzing available data and collaborating with engineers and manufacturing process experts
• Conduct data mining, develop model architectures, train and deploy models, and define metrics aligned with business objectives
• Design, develop, and deploy ETL and data cleansing processes to extract relevant features for modeling (in collaboration with other team members)
• Assist team members in data analysis and interpretation
• Design and conduct experiments to test and validate solutions and models
• Build a production-ready pipeline supporting multiple machine learning models
• Develop monitoring tools for data quality and system performance
• Provide guidance to team members and actively participate in interview processes to hire additional team members