Machine Learning Operations Engineer

ONE STOP COLLECTIBLE CORP

New York, United States

3 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

startup.jobs

Tech stack

Amazon Web Services Computer Vision Microsoft Azure C++ (Programming Language) Cloud Computing Data Security Python (Programming Language) Machine Learning Object Detection Tensorflow Support Vector Machine Reinforcement Learning

+10 more

Google Cloud Pytorch Containerization Scikit Learn Kubernetes Machine Learning Operations TensorRT Multiaccess Edge Computing Recurrent Neural Networks Docker

Job description

Design, develop, and implement end-to-end machine learning pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.
Collaborate with the general software engineering team to integrate ML models into existing software systems and ensure scalability and maintainability.
Work in conjunction with computer vision specialists to apply and optimize ML techniques for image and video analysis, object detection, tracking, and recognition in defense contexts.
Research and evaluate new machine learning algorithms, tools, and technologies to enhance our capabilities and solve challenging problems.
Perform rigorous model testing, validation, and performance tuning to ensure robustness and accuracy in real-world scenarios.
Contribute to the development of best practices for ML engineering, including MLOps, version control, and reproducible research.
Mentor junior engineers and contribute to a culture of continuous learning and knowledge sharing.
Communicate technical concepts effectively to both technical and non-technical stakeholders.

Requirements

Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
Experience: 5+ years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments.
Technical Skills:
Strong proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Solid understanding of core machine learning concepts, including supervised, unsupervised, and reinforcement learning.
Experience with various machine learning model architectures and their application (e.g., CNNs, RNNs, Transformers, decision trees, support vector machines).
Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Experience with MLOps tools and practices.
Experience deploying a variety of edge systems.
Experience with TensorRT and other similar technologies.
Deep knowledge of C++ and Python.
Domain Knowledge:
Experience or strong interest in defense, aerospace, or related industries is highly desirable.
Understanding of the unique challenges and considerations for deploying ML in defense applications (e.g., adversarial robustness, real-time constraints, data security).
Collaboration & Communication:
Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.
Ability to translate complex technical concepts into clear and concise language.
Problem-Solving:
Strong analytical and problem-solving skills, with a proactive and innovative approach.
Ability to work independently and manage multiple priorities in a fast-paced environment., * Experience with specific computer vision tasks such as object detection, segmentation, or tracking.
Familiarity with real-time ML systems and embedded systems.
Contributions to open-source projects or publications in relevant fields.

Benefits & conditions

Competitive salary, equity, and benefits package.
Opportunity to work on cutting-edge technology with a significant impact on national security.
A collaborative work environment that values innovation.
Professional development opportunities and career growth.