

AI Engineer - Machine Learning (contract)
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer - Machine Learning (contract) in Redmond, WA, lasting 12 months with a pay rate of $66 - $75 per hour. Key skills include Python, C++, deep learning libraries, and experience in distributed computing and cloud environments.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
600
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ποΈ - Date discovered
July 8, 2025
π - Project duration
More than 6 months
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ποΈ - Location type
Hybrid
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π - Contract type
Unknown
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π - Security clearance
Unknown
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π - Location detailed
Redmond, WA
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π§ - Skills detailed
#Scala #Datasets #Automated Testing #ML (Machine Learning) #Computer Science #Python #PyTorch #TensorFlow #Azure #C++ #Cloud #AWS (Amazon Web Services) #Deep Learning #AI (Artificial Intelligence) #Distributed Computing
Role description
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AI Engineer - Machine Learning (Contract)
Please note that this is a contract role providing services to Microsoft through external staffing partners of Allegis Global Solutions. If you are selected for this role, you will be employed by AGS and will not be an employee of Microsoft.
Summary
Our team seeks a Machine Learning Engineer to advance the development and improvement of our software foundation and tools vital for training state-of-the-art AI models. Your role will be centered on creating strong, scalable, and efficient training infrastructures and frameworks facilitating the full spectrum of the machine learning process, from handling data to deploying models. In collaboration with researchers and software engineers, you'll ensure that training systems are smoothly integrated and functioning, expanding the limits of AI's capabilities, especially in practical robotics scenarios. Additionally, you will investigate innovative methods to effectively utilize diverse datasets within our training framework.
Job Responsibilities
β’ Create and uphold efficient, scalable, and distributed training systemsβincluding data preprocessing, training orchestration, and model assessmentβfor training large-scale AI models.
β’ Enhance the efficiency of training procedures to improve performance and use of resources, while maintaining scalability and dependability.
β’ Collaborate with researchers to create training and evaluation pipelines for state-of-the-art algorithms.
β’ Develop and design benchmarks for evaluating ML models.
β’ Perform training and and fine-tuning of foundation models for robotic applications .
β’ Monitor and analyze pipelines, identifying bottlenecks and proposing solutions to improve efficiency and performance.
β’ Ensure the robustness and reliability of the training infrastructure, including automated testing and continuous integration.
Requirements
β’ BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
β’ Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
β’ Strong background in distributed computing, parallel processing techniques, handling large-scale datasets and data preprocessing.
β’ Deep understanding of state-of-the-art machine learning techniques and models.
β’ Experience with cloud-based training environments (AWS, Google Cloud, Azure).
β’ Experience in developing and maintaining software tooling and infrastructure for machine learning.
β’ Deep understanding and practical experience with software engineering principles, including algorithms, data structures, and system design.
β’ Experience with continuous integration and automated testing frameworks.
Additional Details
β’ Location: Redmond, WA (Hybrid: 3 days / week onsite)
β’ Duration: 12 Months
β’ Pay Range: $66 - 75 per hour
β’ Weekly Schedule: 40 hours
AGS is an Equal Opportunity/Affirmative Action Employer (M/F/Disability/Veterans). We will consider all applications without regard to race, gender, sexual orientation, gender identity, age, color, religion, national origin, veteran status, disability, genetic information or any other status protected by applicable law.