Summer 2024, Intern: Causal Ai And Machine Learning (Ph.d. Students)

Date Posted:
2024-02-14
Country:
United States of America
Location:
RCCA1: UTRC California Office 2855 Telegraph Avenue, Berkeley, CA, 94705 USA
Position Role Type:
Hybrid
RTX Corporation is an Aerospace and Defense company that provides advanced systems and services for commercial, military and government customers worldwide. It comprises three industry-leading businesses - Collins Aerospace Systems, Pratt & Whitney, and Raytheon. Its 185,000 employees enable the company to operate at the edge of known science as they imagine and deliver solutions that push the boundaries in quantum physics, electric propulsion, directed energy, hypersonics, avionics and cybersecurity. The company, formed in 2020 through the combination of Raytheon Company and the United Technologies Corporation aerospace businesses, is headquartered in Arlington, VA.
To realize our full potential, RTX is committed to creating a company where all employees are respected, valued and supported in the pursuit of their goals. We know companies that embrace diversity in all its forms not only deliver stronger business results, but also become a force for good, fueling stronger business performance and greater opportunity for employees, partners, investors and communities to succeed.
The following position is to join our RTX Research Center (RTRC) team:
The AI Systems Engineering team researches and develops model-based systems engineering, formal methods, planning, decision making, controls, Machine Learning, anomaly detection, and failure analysis solutions for a variety of high impact real world problems in the aerospace, manufacturing, and defense industries.
We are looking for a 2024 summer intern to conduct research in the broad topic of causal AI and Machine Learning, failure and root-cause analysis, and counterfactual analysis relevant to safety-critical systems. You will get to develop state-of-art causal discovery, Machine Learning and AI tools and apply them to real world datasets at scale. Interact and learn from leading researchers on the topic working at RTRC.
Primary Responsibilities

• Algorithm development
• Software implementation and model training for causal discovery and causal inference for failure and anomaly analysis.
• Communicating research results in the form of presentations to internal and external stakeholders and presenting them as written publications.
• Support for publishing results in top conferences.
• Work with a small and focused team on developing failure analysis, causal discovery, and systems engineering tools for deployment of AI in safety-critical AI-enabled systems.
• You will get to learn about the broad areas of AI/ML applications in aerospace and defense.

Required Qualifications

• Two years of PhD level research experience in broad area of causal discovery, causal reasoning, and Machine Learning
• Prior coursework in broad areas of Machine Learning, causal reasoning, and formal methods
• Experience training complex ML pipelines for causal discovery, anomaly detection, and failure analysis applications
• ML software and coding experience in C++, Python, R, SQL, and familiarity with ML frameworks like Pytorch, Tensorflow
• Good understanding of PyWhy ecosystem and experience using available causal discovery and inference libraries such as Causal-Learn, DoWhy, ShowWhy, Tetrad

Education
Candidates must be currently pursuing a PhD degree in computer science, electrical engineering, or related field with a focus on Causal discovery, Machine Learning, AI assurance, V&V and explainability. A minimum 3.8 GPA is required. Please submit a copy of your academic transcripts with your application.
Preferred Qualifications

• Three to Four years of PhD level research experience in combination of the following topics: Causal reasoning, Formal methods, Failure analysis techniques, Anomaly detection, Machine learning. Publication record in top venues like CVPR, NeurIPS, ICLR, AAAI
• Demonstrated mathematical aptitude in the areas of causal discovery, Machine Learning, formal methods
• Good understanding of both type and token casual reasoning techniques and the difference between the two
• Experience applying causal discovery and reasoning techniques to real-world problems
• Ability to set research direction and work independently

Location
Hybrid: Berkeley, CA.
The salary range for this role is 37,000 USD - 82,000 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills.
Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement.
Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance.
This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.
RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.
RTX is An Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age or any other federally protected class.
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