Machine Learning Engineer I bei General Atomics
General Atomics · Rome, Vereinigte Staaten Von Amerika · Onsite
- Junior
- Optionales Büro in Rome
Job Summary
General Atomics (GA), and its affiliated companies, is one of the world’s leading resources for high-technology systems development ranging from the nuclear fuel cycle to remotely piloted aircraft, airborne sensors, and advanced electric, electronic, wireless and laser technologies.
From concept-to-deployment, General Atomics North Point Defense, Inc. (GA-NPD), a division of General Atomics Integrated Intelligence, Inc. (GA-Intelligence), provides AI/ML-based autonomous signal processing and data dissemination solutions providing real-time actionable intelligence supporting tactical and national mission priorities. At GA-NPD, we take a tailored approach meeting our customers’ unique intelligence needs.
We are seeking an MLOps engineer who will streamline the end-to-end machine learning lifecycle, from research, development and experimentation to deployment and monitoring in production environments. This role demands applying software engineering best practices, such as continuous integration (CI) and continuous delivery (CD), to machine learning systems, ensuring ML models are not just developed but are also scalable, reliable, and continuously perform well in real-world applications.
Our team of experts work closely with the end-user in the development and implementation of a defense solution meeting platform and/or site-specific requirements. We pride ourselves as a trusted Defense Industry partner and deliver top-notch services far exceeding typical industry standards.
From concept-to-deployment, General Atomics North Point Defense, Inc. (GA-NPD), a division of General Atomics Integrated Intelligence, Inc. (GA-Intelligence), provides AI/ML-based autonomous signal processing and data dissemination solutions providing real-time actionable intelligence supporting tactical and national mission priorities. At GA-NPD, we take a tailored approach meeting our customers’ unique intelligence needs.
We are seeking an MLOps engineer who will streamline the end-to-end machine learning lifecycle, from research, development and experimentation to deployment and monitoring in production environments. This role demands applying software engineering best practices, such as continuous integration (CI) and continuous delivery (CD), to machine learning systems, ensuring ML models are not just developed but are also scalable, reliable, and continuously perform well in real-world applications.
Our team of experts work closely with the end-user in the development and implementation of a defense solution meeting platform and/or site-specific requirements. We pride ourselves as a trusted Defense Industry partner and deliver top-notch services far exceeding typical industry standards.
DUTIES AND RESPONSIBILITIES:
- Package ML models in containers, i.e. Docker, and deploy to production environments.
- Design and implement ML pipelines for data ingestion, training, evaluation, and deployment.
- Setup and maintain model monitoring and logging of deployed models to track performance metrics like accuracy, latency, and resource utilization.
- Collaborate with a diverse team including data scientists to transition models from research to production, software engineers to integrate ML models into broader application architectures, and system engineers to maximize hardware resources (cpu, fpga, gpu) to optimize performance.
Job Category
Engineering
Travel Percentage Required
0% - 25%
Full-Time/Part-Time
Full-Time Salary
State
New York
Clearance Level
Top Secret (SCI access eligible)
Pay Range Low
81,000
City
Rome
Clearance Required?
Desired
Pay Range High
141,533
Recruitment Posting Title
Machine Learning Ops Engineer
Job Qualifications
- Typically requires a bachelors degree in computer science, engineering, mathematics, or a related technical discipline from an accredited institution. May substitute equivalent machine learning engineer experience in lieu of education.
- Strong proficiency in Python. Experience with other languages like C++ is also valuable.
- Understanding of machine learning principles and frameworks like PyTorch (preferred), TensorFlow, etc.
- Practical experience with Docker for deployment and packaging applications.
- Experience with optimizers such as TensorRT, onnx, and openVino.
- Proficient with Linux command line environment.
- Ability to obtain and maintain DoD Security Clearance is required.
US Citizenship Required?
Yes
Experience Level
Entry-Level (0-2 years)
Relocation Assistance Provided?
No
Workstyle
Onsite
