Carnegie Mellon University (CMU) is looking for a Machine Learning Engineer to join the AI for Autonomy Lab within the SEI AI Division. In this role, you will identify, shape, and lead engineering research to apply AI technologies that improve the performance of critical autonomy systems for U.S. government needs.
What You'll Do
- Work with and lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders.
- Conduct and lead novel prototyping in applied artificial intelligence with a focus on machine learning in autonomy and uncrewed systems (multi-domain).
- Work with AI Division leaders and colleagues to plan, develop, and carry out an overall research and engineering strategy, and to influence the national research and engineering agenda regarding future technology.
- Actively participate on teams of software developers, researchers, designers, and technical leads. Build relationships and collaborate with researchers, government customers, and other stakeholders to understand challenges, needs, possible solutions, and research and engineering directions.
- Contribute to improving the overall technical capabilities of the team by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI AI Division.
What We're Looking For
- BS in Computer Science or related discipline with eight (8) years of experience; MS in the same fields with five (5) years of experience; PhD in Computer Science with two (2) years of experience.
- Must be able and willing to work onsite at an SEI office in Pittsburgh, PA or Arlington, VA 5 days per week.
- Flexible to travel to other SEI offices, sponsor sites, conferences, and offsite meetings on occasion. Moderate (25%) travel outside of your home location.
- Subject to a background investigation and must be eligible to obtain and maintain a Department of War security clearance.
- Deep Technical Knowledge: Extensive research or engineering activities in applied machine learning and artificial intelligence. Worked with tools, techniques, algorithms, software, and programming languages for deep learning, reinforcement learning, statistics, sensors and sensor fusion, planning, computer vision, or related areas. Demonstrated applying systems engineering principles and collaborated across multi-disciplinary project teams. Supported multiple phases of the engineering lifecycle.
- Machine Learning: Profound understanding of machine learning principles and experience applying techniques to real-world problems. Designed and implemented complex machine learning functions and architectures for autonomous systems. Familiar with simulation environments for training and testing models.
- Robotics & Autonomy: Strong understanding of robotics principles and design techniques for air, sea, or land-based vehicles. Experience applying machine learning within these domains. Experience in areas such as sensor fusion, navigation, object search/tracking, collision avoidance, multi-agent collaboration, and human-machine teaming.
- Test & Evaluation: Designed and conducted test and evaluation activities for ML components to assess operational fit and readiness. Experience with model experimentation software, such as MLFlow or Weights & Biases.
- Applied Full-Stack Implementation: Strong development experience. Can design and implement software and systems resources for packaging and managing requirements for AI and ML prototypes. Frequently use tools like Docker. May have experience building applications in cloud platforms (Azure, AWS, Google Cloud Platform).
- Communication and Collaboration: Strong written and verbal communication skills. Can interact collaboratively and diplomatically with customers and colleagues. Can present complex ideas to people without deep subject knowledge.
- Dedication: Can meet deadlines while multi-tasking, sometimes under pressure and with shifting priorities.
- Creativity and Innovation: Creative and curious. Inspired by collaborating with premier technical staff and visionaries. Quickly learns new procedures, techniques, and approaches. Forward-looking and can connect research and engineering with practical challenges.
- Knowledge and Learning: Possess broad technical interests along with a deep knowledge of a particular field such as machine learning, autonomy and adaptive systems, or data analytics.
Nice to Have
- Thought Leadership and Publications: Track record of synthesizing lessons learned from research or engineering activities for publication. Reputation for highest level of research and engineering integrity. Demonstrated contributions and have published research, code (e.g., models, data, software applications), or technical perspectives.
- Familiarity with Emerging Trends and Opportunities: Familiar with technical challenges and emerging trends in computing and information science, and aware of opportunities in industry and government.
- Technical Leadership: Led technical projects and have experience collaborating across research teams and mentoring other researchers.
Technical Stack
- Docker, MLFlow, Weights & Biases, Azure, AWS, Google Cloud Platform
Team & Environment
You will be part of the SEI AI Division’s AI for Autonomy Lab, working on interdisciplinary teams with software developers, researchers, designers, and technical leads. Our culture is creative, curious, and collaborative, focused on meaningful and complex work in a forward-looking environment.
Work Mode
This role is onsite five days per week at either our Pittsburgh, PA or Arlington, VA office location.
Carnegie Mellon University is an equal opportunity employer.
