Top AI & Machine Learning Jobs

Build and ship production AI-powered development features, including agentic workflows, LLM pipelines, retrieval systems, evaluations, fine-tuning, and latency optimization. Own AI feature quality from prototyping through deployment and monitoring. Collaborate with Product, Infrastructure, and Research teams to scale AI capabilities for Bubble’s visual app-building platform.

Develop AI-native industrial automation systems for real-time reasoning, optimization, autonomous operation, and continuous learning at the edge. Design distributed architectures combining generative AI, machine learning, reinforcement learning, streaming analytics, and control systems. Deploy low-latency AI on edge hardware and integrate it with robotics, sensors, controllers, and industrial infrastructure while collaborating with AI research, controls, embedded systems, and operations teams.

Develops medical imaging algorithms and high-performance software for oncology applications. Responsibilities include image registration, segmentation, motion tracking, machine learning, deep learning, numerical optimization, GPU processing, prototype development, validation, documentation, and collaboration with scientists, engineers, healthcare organizations, and product stakeholders.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

Co-develop an EIC Pathfinder proposal focused on trustworthy cognitive AI, including causal reasoning, abstraction, planning under uncertainty, methodology, related work, and evaluation design. The researcher will help build a working TRL 3/4 system, develop benchmarks, and collaborate closely with a startup team through submission and subsequent project activities.

Research and deploy reliable robotic manipulation policies for live operations. Responsibilities include developing policies across multimodal data, debugging transformer and diffusion models, operating real-robot data flywheels, and optimizing inference for approximately 10 Hz performance on edge hardware.

Build and maintain clinical AI benchmarks, evaluation infrastructure, grading systems, and production harnesses. Convert consented patient cases into de-identified evaluation and training data through clinician review workflows. Lead clinical evaluation studies from protocol design through publication, collaborate on reward design and data curation, and develop agentic evaluations covering tool use, long-horizon tasks, memory, records, labs, and escalation behavior.

Design, implement, and maintain scalable ML pipelines for training, deployment, and monitoring. Automate data preprocessing, feature engineering, and validation. Productionize models with data scientists and backend engineers, ensuring reliable, secure, and efficient ML infrastructure and optimized training and inference performance.

Designs, develops, modernizes, deploys, and supports AI-enabled applications and services. Establishes engineering standards for AI-assisted development, implements testing and CI/CD, ensures security, privacy, compliance, and Responsible AI requirements, and monitors application reliability. Collaborates across architecture, security, product, and operations teams, mentors engineers, and evaluates emerging AI technologies, including agentic AI and RAG architectures.