Principal Machine Learning Engineer [Remote]

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Machine Learning Engineer based in United States.

The Principal Machine Learning Engineer will lead the design and delivery of advanced AI and machine learning solutions that shape the future of intelligent technology platforms.
This role offers the opportunity to work on innovative generative AI, large-scale machine learning systems, and data-driven products with global impact.
You will define technical strategies, build production-ready ML solutions, and solve complex challenges across cloud computing, networking, and automation.
The position combines hands-on engineering with technical leadership, allowing you to influence architecture, mentor engineers, and drive innovation.
You will collaborate with scientists, engineers, and cross-functional teams to transform research concepts into scalable real-world applications.
This is an opportunity for an experienced ML leader to contribute to cutting-edge AI initiatives while shaping the next generation of intelligent systems.

Accountabilities:

The Principal Machine Learning Engineer will be responsible for driving machine learning innovation, developing scalable AI solutions, and providing technical leadership across engineering initiatives. Key responsibilities include:

• Define and execute technical vision for machine learning platforms, AI-driven products, and intelligent automation solutions.
• Lead the complete software and machine learning development lifecycle, including architecture, design, implementation, testing, deployment, and operations.
• Develop high-performance, production-ready machine learning systems and extend existing ML frameworks and libraries.
• Design and implement scalable AI solutions for real-time platforms, large-scale data processing, anomaly detection, natural language processing, and intelligent systems.
• Collaborate with engineers and data scientists to accelerate model development, experimentation, validation, and deployment processes.
• Lead technical discussions, architecture reviews, code reviews, and strategic engineering decisions.
• Build and optimize distributed systems using cloud technologies and modern machine learning infrastructure.
• Mentor engineers, provide technical guidance, and contribute to a culture of engineering excellence and innovation.
• Drive improvements in system reliability, scalability, operational efficiency, and engineering practices.
• Stay current with emerging AI technologies and evaluate opportunities to integrate new approaches into products and platforms.

Requirements:

The ideal candidate is a highly experienced machine learning engineer with a strong background in AI systems, distributed computing, and technical leadership. Required qualifications include:

• Bachelor’s degree in Mathematics, Computer Science, or a related technical discipline.
• 12+ years of experience across the full software development lifecycle, including design, coding, testing, deployment, and operations.
• 7+ years of programming experience with strong proficiency in Python, Java, or similar programming languages.
• 5+ years of experience designing and architecting large-scale distributed systems, preferably on cloud platforms such as AWS, Azure, or Google Cloud.
• Extensive experience with machine learning and distributed data technologies such as PyTorch, CUDA, MapReduce, Spark, Flink, Kafka, PySpark, and SageMaker.
• Proven experience leading engineering teams, acting as a technical lead, or mentoring other engineers.
• Strong ability to solve highly complex, ambiguous, and technically challenging problems.
• Experience building end-to-end machine learning solutions, including data preparation, model training, fine-tuning, and deployment.
• Experience working with large-scale datasets and production ML systems.
• Strong communication skills with the ability to influence technical direction and collaborate across teams.

Preferred qualifications include:

• Master’s or PhD degree in Computer Science, Machine Learning, or a related field.
• Experience developing ML optimization techniques using frameworks such as PyTorch and CUDA.
• Experience delivering AI solutions in large-scale enterprise environments.
• Deep understanding of cloud-native architectures and modern AI infrastructure.

Benefits:

The company offers an innovative environment focused on technology advancement, professional growth, and employee success, including:

• Opportunity to work on cutting-edge AI, machine learning, cloud, and automation initiatives.
• Remote work flexibility within the United States.
• Collaborative environment with highly skilled engineers and technical experts.
• Opportunities to influence product strategy and technical roadmaps.
• Professional development and continuous learning opportunities.
• Inclusive workplace culture focused on diversity, collaboration, and innovation.
• Opportunity to contribute to globally impactful technology solutions.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.