Remote Lead ML Engineer — AI Research & Systems

Placements24; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Remote work from anywhere

Executive-level compensation

Health, dental, vision insurance

Stock options

R&D budget

Job summary

Placements24 is seeking a Lead Machine Learning Engineer for a fully remote role. You will guide AI strategy, design and deploy cutting-edge models, and mentor a global team of engineers.

You will drive scalable ML pipelines, advance MLOps practices, and collaborate with researchers to translate findings into real-world applications. Strong theoretical ML knowledge and leadership are essential.

Qualifications

• PhD or Master’s degree in CS, AI, ML or related field.
• 7+ years of ML engineering experience with proven leadership.
• Strong foundations in statistical modeling and ML theory.
• Experience deploying large-scale ML systems.
• Familiar with Spark and cloud platforms (AWS/Azure/GCP).

Responsibilities

• Lead design and implementation of advanced ML models and AI systems.
• Build and optimize scalable ML pipelines for training, evaluation, and deployment.
• Research new ML algorithms and techniques.
• Collaborate with researchers and engineers to translate findings into applications.
• Ensure performance, reliability, and scalability of deployed models.
• Implement MLOps practices, including monitoring, versioning, CI/CD for ML.
• Mentor junior ML engineers.
• Communicate complex technical concepts to diverse stakeholders.
• Stay updated on AI research and industry trends.
• Contribute to strategic direction of the AI research roadmap.

Skills

Mentoring

Strategic leadership

Communication

Python

Education

PhD or Master's in Computer Science / AI / ML

Tools

TensorFlow

PyTorch

Spark

AWS

Azure

GCP

MLOps tools

Job description

Placements24 is seeking a Lead Machine Learning Engineer for a fully remote role. You will guide AI strategy, design and deploy cutting-edge models, and mentor a global team of engineers.

You will drive scalable ML pipelines, advance MLOps practices, and collaborate with researchers to translate findings into real-world applications. Strong theoretical ML knowledge and leadership are essential.