Senior Machine Learning / AI Software Engineer

Senior Machine Learning / AI Software Engineer

Company:

The Boeing Company

The Boeing Company is currently seeking a Senior Machine Learning / AI  Software Engineer to join the Special Projects Dallas (SPD) P-8A Advanced Airborne Sensor (AAS) Software Engineering team located in Richardson, TX. This position will focus on supporting the Boeing Defense, Space & Security (BDS) business organization.

The Boeing Company in Richardson, TX is looking for a Senior Machine Learning / Artificial Intelligence Software Engineer to design, develop, document, and deploy robust, ethical, and scalable AI/ML solutions for aerospace systems. The role requires hands-on ownership across the ML lifecycle: data collection and preprocessing, model design and training, verification and validation, safety risk management, production integration, and ongoing monitoring and improvement. The successful candidate will collaborate closely with cross-functional teams and domain experts, apply best-practice engineering and testing methodologies, and stay current with emerging tools, frameworks, and regulatory/ethical considerations in support for the P-8A Advanced Airborne Sensor (AAS) Mission Systems contracts.

Position Responsibilities:

• Design and implement appropriate AI/ML algorithms and models using standard methods and tools, with attention to ethical implications and potential biases.

• Oversee the collection, cleaning, preprocessing and analysis of large datasets to ensure data quality, reliability and identification of patterns, trends, and insights.

• Train, evaluate, and optimize model performance, generalization, and computational efficiency.

• Test, document, debug, and validate AI/ML models and associated software systems following engineering standards.

• Consult on training, evaluation and optimization of performance and capabilities of Artificial Intelligence models

• Oversee Safety Risk Management processes for Artificial Intelligence models in accordance with organizational standards.

• Provide subject matter expertise on integration and deployment of standard efficient and scalable Artificial Intelligence models into production environments.

• Oversee monitoring, validation and improvement of standard in production Artificial Intelligence models.

• Consult and collaborates with cross functional teams and domain experts to understand business requirements, gather feedback, and iterate Artificial Intelligence models and algorithms.

• Influence  current and emerging technologies, tools, frameworks, and regulations in the Artificial Intelligence environment and contributes to Artificial Design Practice(s).

• Research, prototype, and adopt current and emerging technologies, tools, frameworks, and regulatory guidance relevant to AI/ML and aerospace applications.

Basic Qualifications (Required Skills/Experience):

• Masters degree or higher in Engineering and/or a Technical discipline

• Proficiency with ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and common data tooling (e.g., pandas, NumPy).

• Experience with data engineering tasks: data collection, cleaning, preprocessing, feature engineering, and working with large-scale datasets.

• Solid understanding of ML fundamentals: supervised and unsupervised learning, deep learning, model evaluation metrics, regularization, and hyperparameter tuning.

• Hands-on experience building end-to-end ML solutions from data ingestion through production deployment and maintenance.

Preferred Qualifications (Desired Skills/Experience):

• PHd degree in Computer Science, Electrical Engineering, Data Science, or related field (or equivalent experience).

• Significant experience designing and deploying ML systems in production, preferably in regulated domains.

• Strong knowledge of ML algorithms (supervised, unsupervised, deep learning), model evaluation, and optimization techniques.

• Proficiency with data engineering: cleaning, feature engineering, and working with large-scale datasets.

• Experience with ML frameworks and tooling (e.g., TensorFlow, PyTorch, scikit-learn), containerization, CI/CD, and cloud or on-prem deployment pipelines.

• Experience with GPU utilization

• Experience with real-time requirements regarding computational efficiency

• Familiarity with safety engineering processes, risk assessment, and methods to assess and mitigate model bias and ethical risk.

• Excellent coding skills in relevant languages (e.g., Python, C++/Java as applicable), and software eng