Job description

Systems / Machine Learning Engineer Location: Remote (US Only) Duration: 12 months, with potential for extension based on experience and business needs Experience Level: Early Career / 0–1 years Pay Rate: $50 - $55/hr on W2 About the Role We are seeking a highly motivated Systems / Machine Learning Engineer to join a highly interdisciplinary Fundamental AI Research (FAIR) organization focused on advancing artificial intelligence through research breakthroughs and bringing the latest AI/ML advancements to real-world products and experiences. In this role, you will work alongside research scientists, engineers, and cross-functional partners to build the systems, tools, and infrastructure that enable cutting-edge AI/ML research. You will contribute across the full machine learning development lifecycle — including model training, data collection, evaluation, software development, research tooling, codebase improvements, and troubleshooting. This is an excellent opportunity for an early-career engineer or recent graduate to gain hands-on experience working with advanced AI/ML systems and collaborating with experienced researchers and engineers. Day-to-Day Responsibilities • Engineer, design, implement, and improve machine learning systems and tools that enable advanced AI research. • Develop and maintain clean, robust, and maintainable machine learning code . • Work with deep learning codebases supporting the training and evaluation of advanced AI/ML models. • Apply knowledge of machine learning and relevant research domains to platform and framework development . • Develop ML algorithms, infrastructure, and engineering tools using Python, PyTorch, and/or C/C++ . • Support research teams with model training, data collection, evaluation, experimentation, and troubleshooting . • Contribute throughout the ML research engineering lifecycle, from development and testing through evaluation and code sharing. • Collaborate with scientists, engineers, and cross-functional partners to translate research concepts into reliable engineering systems. • Troubleshoot existing systems and improve their performance, reliability, and usability. Must Have Qualifications: The following qualifications are required: • 0–1 years of deep learning experience , including experience working with codebases supporting AI/ML model development, training, or evaluation. • Experience developing machine learning algorithms or infrastructure using Python, PyTorch, and/or C/C++ . • Bachelor's degree in Computer Science, Computer Engineering, or a related technical field . • Strong programming fundamentals and the ability to write clean, robust, and maintainable code . • Demonstrated experience with machine learning or deep learning through academic research, coursework, internships, personal projects, or equivalent experience. Preferred Qualifications: • Demonstrated software engineering track record through professional experience, coding competitions, academic projects, or meaningful open-source/GitHub contributions . • Experience working with advanced AI/ML or deep learning models and research-oriented codebases. • Experience developing ML infrastructure, frameworks, tooling, or evaluation systems . • Strong experience with PyTorch or other modern deep learning frameworks. • Experience with C/C++ in addition to Python. • Master's degree, PhD, or other advanced degree in Computer Science, Computer Engineering, Machine Learning, Artificial Intelligence, or a related technical field . • Experience working in an academic or research environment involving machine learning, deep learning, or AI systems . What You'll Gain • Hands-on experience working with state-of-the-art AI/ML systems and research technologies . • The opportunity to collaborate with experienced research scientists and software engineers . • Exposure to the complete ML research engineering lifecycle , beyond model training alone. • Opportunities to develop expertise in machine learning infrastructure, frameworks, evaluation, and research tooling . • Experience contributing to AI/ML research and technology with potential applications at significant scale. Interview Process The interview process is expected to include 1–2 rounds , with a primary one-hour technical and behavioral interview . Candidates should be prepared to discuss their: • Machine learning/deep learning experience • Programming skills • Academic or personal ML projects • Software engineering fundamentals • Approach to solving technical problems Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Los Angeles Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, qualified applicants will be considered for assignment with arrest and conviction records. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness, meet client expectations, standards, and accompanying requirements, and safeguard business operations and company reputation. #TMMT

Requirements

Must-haves

Systems / Machine Learning Engineer
Location: Remote (US Only)
Duration: 12 months, with potential for extension based on experience and business needs
Experience Level: Early Career / 0–1 years
Pay Rate: $50 - $55/hr on W2
In this role, you will work alongside research scientists, engineers, and cross-functional partners to build the systems, tools, and infrastructure that enable cutting-edge AI/ML research.
This is an excellent opportunity for an early-career engineer or recent graduate to gain hands-on experience working with advanced AI/ML systems and collaborating with experienced researchers and engineers.
Day-to-Day Responsibilities
Engineer, design, implement, and improve machine learning systems and tools that enable advanced AI research.
Develop and maintain clean, robust, and maintainable machine learning code .
Work with deep learning codebases supporting the training and evaluation of advanced AI/ML models.
Apply knowledge of machine learning and relevant research domains to platform and framework development .

Nice-to-haves

Demonstrated software engineering track record through professional experience, coding competitions, academic projects, or meaningful open-source/GitHub contributions .
Experience working with advanced AI/ML or deep learning models and research-oriented codebases.
Experience developing ML infrastructure, frameworks, tooling, or evaluation systems .
Strong experience with PyTorch or other modern deep learning frameworks.
Experience with C/C++ in addition to Python.
Master's degree, PhD, or other advanced degree in Computer Science, Computer Engineering, Machine Learning, Artificial Intelligence, or a related technical field .
Experience working in an academic or research environment involving machine learning, deep learning, or AI systems .
What You'll Gain
Hands-on experience working with state-of-the-art AI/ML systems and research technologies .
The opportunity to collaborate with experienced research scientists and software engineers .
Exposure to the complete ML research engineering lifecycle , beyond model training alone.
Opportunities to develop expertise in machine learning infrastructure, frameworks, evaluation, and research tooling .

Application prep

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Keywords to mirror

machine learningaimlpython

Platform: jobdiva
Effort: medium