Top Machine Learning Jobs in Philadelphia, PA

The role involves developing machine learning models for Netflix's Studio, collaborating with teams, and scaling ML solutions for data analytics.

As a Principal Machine Learning Engineer, you will design and develop machine learning applications, lead teams, and ensure project management excellence while engaging with clients.

The ML Engineering Intern will contribute to the Nebula LLM development, tackle machine learning challenges, and gain hands-on experience in AI and language technology.

Design and scale systems for personalization and ranking using ML. Mentor engineers and lead initiatives in a consumer-focused environment, leveraging AI tools.

As a Machine Learning Engineer, you will develop and deploy AI/ML solutions, collaborate with product managers and engineers, and improve learning experiences.

Design, implement, and maintain ML solutions using AWS SageMaker. Develop end-to-end ML pipelines and collaborate on model deployment to enhance data-driven decision-making.

The Senior Machine Learning Engineer will design, prototype, build AI systems, evaluate ML models, and improve Vectara's AI Agents with a focus on reliability and performance.

Seeking a machine learning engineer to build solutions for personalization, automated experimentation, predictive modeling, and content generation, impacting customer interactions.

Lead the Notifications Relevance team, developing machine learning models for personalization and user engagement, while managing engineering efforts and team growth.

The engineer will build end-to-end applications, analyze data trends, deploy solutions, and improve process efficiency for the solar industry.

Develop ML models and pipelines for autonomous tractor initiative, improving system safety and performance in production environments.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

As an ML Engineer, you will build and deploy machine learning models for autonomous robots, collaborating with ML scientists and engineers to enhance AI capabilities.

Develop and optimize large-scale ML models and training pipelines for automating PCB layouts, collaborating on data pipeline design and novel architectures.

The Senior AI/ML Engineer will design and maintain scalable ML infrastructure, mentor data scientists, and ensure production readiness of AI models.

Design and deploy machine learning models for autonomous vehicles, focusing on data tagging, dataset curation, and model validation.

The engineer will design and maintain auto-bidding algorithms, optimize ad delivery systems, and collaborate on engineering projects for campaign performance.

The Solutions Architect for AI/ML at Snowflake will advise clients on best practices for Data Science workloads, design solutions, and build ML pipelines. Responsibilities also include working hands-on with SQL and Python, maintaining technical expertise and collaborating with teams to improve products.

The Senior Machine Learning Engineer will develop scalable ML systems, lead a team, mentor juniors, and work cross-functionally to enhance customer experience through machine learning solutions.

The Machine Learning Engineer will fine-tune and deploy Large Language Models, optimize ML models, maintain data pipelines, and collaborate on AI features.

The Staff Machine Learning Engineer will design and deploy ML models, build real-time model APIs, and collaborate across teams to drive user growth and revenue through AI/ML solutions.

The role involves developing scalable ML solutions, leading cross-functional discussions, analyzing knowledge data, and producing technical documentation to improve search relevance using AI and machine learning technologies.

Contribute to AI/ML capabilities for personalization in Yahoo Mail, develop tools for data processing, and collaborate with cross-functional teams.

The Senior Machine Learning Engineer will develop ML platforms, optimize model lifecycles, lead initiatives, and maintain code quality, collaborating with Data Science teams.

The role involves building scalable AI features, optimizing machine-learning pipelines, evaluating LLM outputs, and ensuring compliance with data governance standards.