Applied Machine Learning Engineer

The Aerospace Corporation is hiring a full-time Applied Machine Learning Engineer for 100% onsite work in Chantilly, VA.

Responsibilities

• Support customers spanning National Security Space (e.g., AF, NRO) through civil (e.g., NASA, NOAA) and commercial domains (commercial space, autonomous vehicles, etc.)
• Apply data science and AI knowledge across space enterprise capabilities and align work with corporate workforce development and strategic focus areas
• Evaluate AI/ML technologies for scalable, resilient, mission-critical production environments
• Coordinate the development and execution of AI/ML experiments
• Coordinate development of proof-of-concept infrastructure configurations and software prototypes
• Collaborate with small, innovative teams to deliver features and products
• Present results through written and verbal communication to team members and stakeholders
• Reinforce a culture focused on learning and progress with team members and others
• Apply accountability and innovation through leadership competency development
• Maintain ongoing learning to stay current with government missions and the evolving data science and AI landscape
• Continue domain education (e.g., physics, math, and/or computer science) while staying aligned with existing systems and future mission objectives
• Note that duties and responsibilities may change or new assignments may be added as needed

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Information Systems, Electrical or Computer Engineering, or related technical field(s)
• 5 or more years of experience in a Data Scientist, Machine Learning Engineer, or AI/ML Researcher role (any domain)
• Strong proficiency in at least two programming languages (examples: Python, R, C/C++)
• Familiarity with ML frameworks and experience with machine learning and deep learning libraries in Python
• Hands-on experience with container orchestration tooling (e.g., Docker, Kubernetes)
• Experience with software engineering concepts with an AI focus (e.g., MLOps/DevOps, ML Development Lifecycle, data structures)
• Working knowledge of Unix/Linux operating systems
• Ability to obtain and maintain a security clearance issued by the US government (U.S. citizenship required)
• 8 or more years of experience in a Data Scientist, Machine Learning Engineer, or AI/ML Researcher role (any domain)
• Demonstrated experience architecting, designing, or implementing enterprise-scale AI/ML solutions with multiple tenants and cloud integration
• Hands-on experience with MLOps and data engineering tools (e.g., MLFlow, Kubeflow, Neptune, Airflow, DVC)
• Strong preference for an advanced degree in a related field(s) (Computer Science, Computer Information Systems, Electrical or Computer Engineering, or related)
• Strong preference for experience designing or managing enterprise-level ML platforms and production-grade ML models at scale
• Strong preference for familiarity with HPC hardware for ML (GPUs, TPUs), CUDA programming, and advanced ML optimization techniques and architectures
• Strong preference for demonstrated ability to apply judgment and critical thinking in a scientific discipline
• Strong preference for experience implementing and guiding teams toward software development best practices
• Strong preference for experience with SQL, NoSQL, Cypher, and other big data querying languages
• Strong preference for experience with big data frameworks (e.g., Hadoop, Spark, Flink)
• Strong preference for familiarity with data pipelining and streaming technologies (e.g., Apache Kafka, temporal)
• Strong preference for demonstrated contributions to open-source software repositories (e.g., GitHub, Kaggle)
• Strong preference for experience deploying ML models on cloud platforms (e.g., AWS, Azure)
• Strong preference for domain expertise aligned to one or more customer mission areas (e.g., USSF, NRO)
• Strong preference for an active security clearance

Technologies

• Python, R, C/C++
• Docker, Kubernetes
• Unix/Linux
• MLOps, DevOps, ML Development Lifecycle
• MLFlow, Kubeflow, Neptune, Airflow, DVC
• SQL, NoSQL, Cypher
• Hadoop, Spark, Flink
• Apache Kafka, temporal
• github, kaggle
• AWS, Azure
• GPUs, TPUs, CUDA
• MLOps/DevOps (noted separately), container orchestration tooling

Benefits

• Comprehensive health care and wellness plans
• Paid holidays, sick time, and vacation
• Standard and alternate work schedules, including telework options
• 401(k) Plan: total company-paid benefit of 8%, 10%, or 12% of eligible compensation based on years of service and matching contributions; immediately eligible and vested upon hire
• Flexible spending accounts
• Variable pay program for exceptional contributions
• Relocation assistance
• Professional growth and development programs
• Education assistance programs
• Inclusive work environment built on teamwork, flexibility, and respect

Leadership Competencies

• Operate Strategically
• Lead Change
• Engage with Impact
• Foster Innovation
• Deliver Results