Machine Learning Engineer

Posted 20 Hours Ago
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Cambridge, MA
Hybrid
Mid level
Aerospace • Artificial Intelligence • Computer Vision • Machine Learning • Software • Defense
Welcome to MORSE!
The Role
As a Machine Learning Engineer, you will design, implement, and manage complex ML algorithms focusing on computer vision. Responsibilities include acquiring truth data, integrating and testing algorithms, analyzing data from tests, and developing advanced algorithms to support US National Security programs.
Summary Generated by Built In

We are seeking a Machine Learning Engineer to join our team at MORSE.  You will play a pivotal role in designing, implementing, and managing complex ML algorithms and systems, with a focus on computer vision (CV) and other types of data. You will be responsible for acquiring truth data, integrating algorithms, testing algorithms, combining algorithms, reviewing literature to stay on top of the latest-and-greatest methods, analyzing data from field tests, and developing advanced algorithms.  MORSE’s AI & ML work crosses modalities, and experience or interest in the fields of Large Language Models (LLM), audio analysis, computer vision, and advanced reasoning is a plus. You will work with MORSE’s current team of engineers to transition algorithms to production, which may run on on-prem servers, on the cloud, or on a real-time embedded system. You will be part of our team working to accelerate our US National Security customers abilities to use natural language processing capabilities in mission-critical environments. 

Responsibilities: 

  • Develop, fine-tune, train, and optimize Computer Vision algorithms processing tasks such as object detection and tracking.  
  • Use MLOps tools for efficient experiment tracking, data management, and reproducibility 
  • Write robust, efficient, and maintainable code 
  • Track the latest advancements with machine learning research to bring new techniques and methodologies to MORSE 
  • Conduct experiments and perform rigorous evaluations to assess the effectiveness and efficiency of CV models 


Skills and Requirements: 

  • US CITIZENSHIP REQUIRED and the ability to obtain a U.S. Security Clearance 
  • Masters or Ph.D. in Computer Science, Computer Engineering, Data Science, Aerospace, Mathematics, Physics, or related field 
  • Proven experience in applying CV models, techniques, frameworks, and libraries to implement and fine-tune models 
  • Proven experience testing and validating the performance of AI technologies in real-world applications 
  • Proficiency in Python  
  • Experience with cloud platforms (AWS and Azure)  
  • Experience with Docker 
  • Experience with MLOps tools such as Airflow, MLFlow, AimStack, etc. 
  • Exceptional communication skills and the ability to work well with customers 
  • Understanding of Department of Defense requirements and standards is a plus 

 

MORSE is an innovative, employee-owned, tech company specializing in solving multi-disciplinary problems faced by the US National Security Ecosystem. Our specially selected team of engineers, software developers and scientists develop algorithms, software, and hardware in the domains of AI, Manned and Unmanned Vehicles, Mission Planning, and Warfighter Situational Awareness. 

The team at MORSE takes pride in being the smart team that is easy to work with. We focus on steady, long-term success while maintaining a collaborative, enjoyable work experience for its employees. Our team values work-life balance through flexibility and other programs, along with a comprehensible benefits package for employees and families. For more information, please visit www.morsecorp.com. 

 

 

Top Skills

Python
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The Company
HQ: Cambridge, MA
180 Employees
Hybrid Workplace
Year Founded: 2014

What We Do

MORSE is a specially selected team of scientists, engineers, and software developers who use asymmetric and unconventional approaches to deploy practical solutions that solve difficult multi-disciplinary problems faced by the US National Security Ecosystem.

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MORSE Corp Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Most of our staff are in the office 3+ days a week

Typical time on-site: Flexible
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HQCambridge, MA
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Arlington, VA office
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