Top Artificial Intelligence Machine Learning Engineer Jobs

Develop and deploy state-of-the-art physical AI for outdoor robotics, focusing on perception, planning, control, simulation-driven training, multi-sensor integration, data pipelines, and GPU-optimized training and inference.

Design and productionize algorithms and regression models for real-time bidding and conversion prediction; apply privacy-preserving clustering to conversational data; analyze data to improve ad matching; conduct research, form hypotheses, and communicate findings to technical and non-technical stakeholders.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

As an AI/ML Engineering Intern, you'll assist in building data pipelines, optimizing distributed systems, and deploying machine learning applications, gaining hands-on experience with advanced AI technologies.

The Sr. Machine Learning Engineer will use ML and DL models to solve complex problems, mentor junior engineers, and assess algorithm performance while collaborating with a team.

Sift Healthcare seeks a Senior AI Engineer to enhance ML and AI infrastructure, improve predictive models for healthcare payments, and collaborate with cross-functional teams, requiring 5+ years in an AI role and advanced degree.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.