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The Aerospace Corporation

Machine Learning Engineer

Job Posted 9 Days Ago Reposted 9 Days Ago
Hybrid
Chantilly, VA
Senior level
Hybrid
Chantilly, VA
Senior level
The Machine Learning Engineer will develop and deploy AI solutions, evaluate new technologies, and collaborate with teams on machine learning projects while ensuring adherence to security protocols.
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The Aerospace Corporation is the trusted partner to the nation's space programs, solving the hardest problems and providing unmatched technical expertise. As the operator of a federally funded research and development center (FFRDC), we are broadly engaged across all aspects of space- delivering innovative solutions that span satellite, launch, ground, and cyber systems for defense, civil and commercial customers. When you join our team, you'll be part of a special collection of problem solvers, thought leaders, and innovators. Join us and take your place in space.
Information Systems and Cyber Division (ISCD) staff couple the latest in information system technologies, such as elastic compute clouds, containerization, microservices, real-time operating systems, and visualization frameworks, with expertise in cyber security, software architecture, software engineering, data science, Artificial Intelligence, process improvement, and software development to deliver responsive, resilient, high-performance software intensive systems to our Intelligence Community, DoD, and civilian customers.
The Data Science and Artificial Intelligence Department (DSAID) seeks a creative and enthusiastic Machine Learning Engineer to join a diverse team of engineers, data scientists, and programmers with a passion for researching, prototyping, understanding, and building AI and data enabled tools across the space enterprise. We are a growing, innovative, and collaborative department that makes meaningful contributions to National Security Space (AF, NRO, etc.), and Civil and Commercial customers (NASA, MDA, DHS, etc.).
This positions specifically is for the Machine Learning Engineering section, where we focus on translating cutting-edge AI research into robust, scalable, and production-ready machine learning solutions. We bridge the gap between theoretical ML solutions and real-world impact by optimizing and scaling ML models, designing and building robust ML software architectures, providing expertise in harnessing the latest advancements in compute hardware, and implementing MLOps and Trusted AI best practices.
Machine Learning Engineers contribute to projects involving one or more of the following disciplines: Data Science, Applied Machine Learning, Deep Learning, Machine Learning Architecture Design, Machine Learning Operations (MLOps), Natural Language Processing, Artificial Intelligence, and Computer Vision
Work Model
This is a fulltime position based in Chantilly, Va, offering a hybrid work model that combines 3 regular onsite workdays and remote flexibility as the business needs allow.
What You'll Be Doing

  • DSAID's customers range from National Security Space (AF, NRO, etc.) to civil (NASA, NOAA, etc.) and commercial (commercial space, autonomous vehicles, etc.)
  • DSAID applies data science and AI knowledge across the space enterprise, to Aerospace enterprise capabilities, and towards corporate workforce development and strategic focus areas
  • Machine Learning Engineers learn and develop their skills by working on teams spanning disciplines, experience levels, and organizational boundaries.
  • Primary functions include:
    • Evaluation of technologies for use in scalable and resilient mission-critical applications in a production environment
    • Coordinated development and execution of experiments
    • Coordinated development of proof-of-concept infrastructure configuration and software prototypes
    • Collaboration with small, innovative teams to deliver features and products
    • Written and verbal presentation of results to team members and stakeholders
    • Reinforcing an environment of learning and progress with team members and others
    • Focus on accountability and innovation in leadership competency development
  • Duties, responsibilities and activities may change, or new ones may be assigned as needed
  • Machine learning engineers must maintain a commitment to ongoing learning in order to stay current with government missions and the ever-changing climate of data science and AI. This requires both ongoing education in relevant domains, such as physics, math, and/or computer science, as well as an understanding of the existing systems and future objectives of government missions.


What You Need to be Successful
Minimum Requirements for the Machine Learning Engineer- Senior Member of Technical Staff

  • Bachelors degree STEM or in Computer Science, Computer Information Systems, Electrical or Computer Engineering or related technical field(s)
  • 5 or more years of experience in Data Scientist or Machine Learning Engineer or AI/ML Researcher role in any domain
  • Experience developing and deploying ML applications/solutions
  • Experience with at least two MLOPs and Data Engineering tools (e.g., MLFlow, Kubeflow, Neptune, Airflow, DVC)
  • Software development skills in at least two different programming languages (e.g. Python, R, C/C++), including familiarity with machine learning and deep learning libraries in Python
  • Experience with container orchestration tooling(Docker, Kubernetes, etc.)
  • Experience with and understanding of software engineering concepts with an AI focus (MLOps/DevOps, ML Development Lifecycle, data structures, etc.)
  • Experience with and understanding of machine learning and statistics
  • Working knowledge of Unix/Linux operating systems
  • This position requires ability to obtain and maintain a security clearance, which is issued by the US government. U.S citizenship is required to obtain a security clearance.


In addition to the above, the minimum requirements for the Machine Learning Engineer- Engineering Specialist :

  • Advanced degree in Computer Science, Computer Information Systems, Electrical or Computer Engineering, or related field(s)
  • 8 or more years of experience in Data Scientist or Machine Learning Engineer or AI/ML Researcher role in any domain
  • Experience managing or leading teams on machine learning projects.
  • Experience in architecting scalable machine learning platforms and solutions.


How You Can Stand Out
It would be impressive if you have one or more of these:

  • Advanced degree in Computer Science, Computer Information Systems, Electrical or Computer Engineering, or related field(s)
  • Experience in designing or managing enterprise-level ML platforms and production-grade ML models at scale
  • Familiarity with High Performance Computing hardware (GPUs, TPUs)
  • Demonstrated ability to exercise judgement and critical thinking in a scientific discipline
  • Experience implementing and guiding teams toward software development best practices
  • Experience in SQL, NoSQL, Cypher and other big data querying languages
  • Experience with big data frameworks (Hadoop, Spark, Flink etc.)
  • Experience with ML lifecycle management tools (MLflow, Kubeflow, etc.)
  • Familiarity with data pipelining and streaming technologies (Apache Kafka, Apache Nifi, etc.)
  • Demonstrated contributions to open-source software repositories (github, kaggle, etc.)
  • Experience deploying ML models on cloud platforms (AWS, Azure, etc.)
  • Domain expertise relevant to one or more customer organization mission areas (USSF, NRO, etc.)
  • Active security clearance


Leadership Competencies
Our leadership philosophy is simple: every employee, regardless of level and role, can demonstrate leadership. At Aerospace, our commitment is our people. To cultivate our talent and ensure that we have a strong pipeline of future leaders, we want individuals who:

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


Ways We Reward Our Employees
During your interview process, our team will provide details of our industry-leading benefits.
Benefits vary and are applicable based on Job Type. A few highlights include:

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


We are all unique, from various backgrounds and all walks of life, yet one thing bonds all of us to each other-the belief that we can make a difference. This core belief empowers us to do our best work at The Aerospace Corporation.
Equal Opportunity Commitment
The Aerospace Corporation is an equal opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, age, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender, gender identity or expression, color, religion, genetic information, marital status, ancestry, national origin, protected veteran status, physical disability, medical condition, mental disability, or disability status and any other characteristic protected by state or federal law. If you're an individual with a disability or a disabled veteran who needs assistance using our online job search and application tools or need reasonable accommodation to complete the job application process, please contact us by phone at 310.336.5432 or by email at peoplemangmnt.mailbox@aero.org . You can also review Know Your Rights: Workplace Discrimination is Illegal.

Top Skills

Airflow
Apache Kafka
Apache Nifi
C/C++
Docker
Dvc
Flink
Hadoop
Kubeflow
Kubernetes
Mlflow
Neptune
Python
R
Spark

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