Top Machine Learning Engineer Jobs

This role involves developing ML tools and frameworks, supporting data science environments, and leading projects from design to execution. Candidates need strong programming skills and a quantitative background.

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

The Senior Staff Machine Learning Engineer will improve Coupang's search and discovery systems using advanced algorithms, addressing bottlenecks, proposing solutions, and leading projects involving personalization and recommendations.

The Applied ML Engineer will research and build AI/ML systems, own model development, partner with teams to impact marketplace metrics, and establish best practices for AI/ML at Paraform.

Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking in maritime intelligence.

Lead the design and production of ML systems for Pinterest's ads measurement products. Collaborate with various teams to improve measurement quality and contribute to technical direction and best practices.

Design and implement ML algorithms for bidding and ranking systems in ads. Own complete ML systems from conceptualization to production. Collaborate with teams to integrate models and enhance advertising value using AI.

Design and implement AI agents for mission planning and orchestration across autonomous systems, focusing on LLM frameworks and edge computing.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

The role focuses on optimizing and deploying generative AI models in production environments, working directly with customers to ensure optimal performance and integration solutions.

As a Machine Learning Engineer Intern, you'll tackle challenges in ML including training models, optimizing inference on GPU, and documenting findings.

The role involves training and fine-tuning vision and language models, building evaluation and learning pipelines, and optimizing model performance.

As a ML Engineer at Apiphany, you will develop advanced machine learning models, focusing on engineering data, AI systems, and real-world performance.

The Machine Learning Engineer will design and build scalable ML data pipelines, manipulate 3D data, collaborate with customers, and ensure reliable deployment of ML systems.

The Machine Learning Engineer role at Wayve focuses on leading initiatives in model-based autonomous driving, involving the development and delivery of ML-driven behaviors, collaboration across teams, and mentoring other engineers.

Develop, validate, and deploy machine learning models and scalable ML pipelines. Preprocess and analyze data, run experiments, collaborate with senior engineers and stakeholders, and support model risk governance and regulatory-compliant AI applications (fraud detection, credit scoring, marketing analytics).

The Staff Machine Learning Engineer will optimize the inference layer for voice applications, focusing on model serving and real-time audio processing. Responsibilities include driving performance architecture, collaborating with model partners, and developing evaluation frameworks for STT and TTS models.

The role involves designing large-scale data pipelines for machine learning, improving ML workflows, and ensuring efficiency in distributed model training environments. Key responsibilities include developing infrastructure, integrating ML pipelines, optimizing performance, and collaborating with ML engineers on large-scale experimentation.

Design and operate large-scale ML data pipelines, develop infrastructure for distributed training workflows, and ensure reliability and performance of the offline ML platform.

Design and operate large-scale data pipelines for ML training, integrating orchestration tools, optimizing performance, and leading architectural improvements.

The role involves designing large-scale offline platforms for ML training, developing data pipelines, ensuring scalability and performance, and integrating orchestration systems.

Design, build, and deploy end-to-end ML and generative AI solutions for clients: data exploration, model training/evaluation, production deployment, MLOps, monitoring, and advisory. Apply LLMs (RAG, embeddings, fine-tuning), establish CI/CD and responsible-AI practices, present findings to stakeholders, and mentor teammates while contributing across engineering and consulting tasks.

Build and enhance an ML platform supporting the full model lifecycle, scalable training and real-time prediction services, expand NLP capabilities, design data engineering solutions, maintain production reliability, and mentor junior engineers.

As an ML Infrastructure Engineer, design backend components, enhance platform performance, and drive system improvements while collaborating across teams.

As a Senior ML Engineer, you'll design and deploy advanced machine learning systems for autonomous vehicles, ensuring real-time performance and robustness in diverse conditions. You'll lead technical projects, mentor engineers, and collaborate across teams to enhance onboard capabilities.