Machine Learning Engineer

MORSE Corp
Cambridge, United States of America
3 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Compensation
$ 210K

Job location

Cambridge, United States of America

Tech stack

Clean Code Principles
Artificial Intelligence
Airflow
Algorithm Design
Amazon Web Services (AWS)
Data analysis
Computer Vision
Azure
Cloud Computing
Computer Engineering
Python
Machine Learning
Natural Language Processing
Object Detection
Large Language Models
Information Technology
Data Management
Machine Learning Operations
Docker

Job description

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

Requirements

  • SHIP 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

Benefits & conditions

MORSE Corp's salary range for this role carefully considers a wide range of compensation factors, including but not limited to, prior experience, education, skills and expertise, location, internal equity, and other factors that are job related and consistent with business need. Therefore, final offer amounts may vary from the amount stated. Depending on role eligibility, total compensation may also include bonus, stock, 401(k) match, paid time off, medical, dental, vision and life insurance.

Employees also receive 10 paid holidays per year. MORSE maintains an "open" leave policy that does not restrict exempt, regular full-time employees to a specific number of paid sick or vacation days. However, this policy is not an "unlimited" paid leave policy.

About the company

MORSE Corp is an employee owned, small business based in Cambridge, MA, Arlington, VA, and Seattle, WA with a history of fielding cutting-edge technology. MORSE boasts a specially selected team of scientists, engineers, and software developers to deliver best-in-class technical solutions that solve difficult multidisciplinary problems faced by the US National Security Ecosystem.

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