Systems / Machine Learning Engineer

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 motivatedSystems / Machine Learning Engineer to join a highly interdisciplinaryFundamental 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 thefull machine learning development lifecycle — including model training, data collection, evaluation, software development, research tooling, codebase improvements, and troubleshooting.
This is an excellent opportunity for anearly-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 improvemachine learning systems and tools that enable advanced AI research.
• Develop and maintainclean, 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 toplatform and framework development.
• Develop ML algorithms, infrastructure, and engineering tools usingPython, PyTorch, and/or C/C++.
• Support research teams withmodel 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 developingmachine learning algorithms or infrastructure usingPython, PyTorch, and/or C/C++.
• Bachelor's degree inComputer Science, Computer Engineering, or a related technical field.
• Strong programming fundamentals and the ability to writeclean, robust, and maintainable code.
• Demonstrated experience withmachine learning or deep learning through academic research, coursework, internships, personal projects, or equivalent experience.

Preferred Qualifications:

• Demonstratedsoftware engineering track record through professional experience, coding competitions, academic projects, or meaningfulopen-source/GitHub contributions.
• Experience working withadvanced AI/ML or deep learning models and research-oriented codebases.
• Experience developingML infrastructure, frameworks, tooling, or evaluation systems.
• Strong experience withPyTorch or other modern deep learning frameworks.
• Experience withC/C++ in addition to Python.
• Master's degree, PhD, or other advanced degree inComputer Science, Computer Engineering, Machine Learning, Artificial Intelligence, or a related technical field.
• Experience working in an academic or research environment involvingmachine learning, deep learning, or AI systems.

What You'll Gain

• Hands-on experience working withstate-of-the-art AI/ML systems and research technologies.
• The opportunity to collaborate with experiencedresearch scientists and software engineers.
• Exposure to the completeML research engineering lifecycle, beyond model training alone.
• Opportunities to develop expertise inmachine 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 include1–2 rounds, with a primaryone-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