Top AI & Machine Learning Jobs

Build and own Python-based performance and power modelling frameworks for photonic AI hardware. Model concurrency, event-driven behavior, compute, memory, interconnects, and accelerator blocks; characterize AI workloads against hardware and RTL results; consume MLIR and compiler outputs; analyze simulation traces and benchmarks; validate power models; maintain CI/CD and simulation flows; and guide architecture and compiler decisions while developing future team members.

Lead product strategy, roadmap, OKRs, and execution for an AI product pod. Partner with design, engineering, marketing, and customer success to develop user-facing AI capabilities from discovery through launch and iteration. Conduct user research, define MVPs, evaluate product quality, prioritize initiatives, mentor junior product managers, and improve internal AI-enabled product development practices.

The Machine Learning Engineer II will design, develop, and maintain machine learning models, lead ML initiatives, collaborate across teams, and mentor junior members while working on Insider Risk Management solutions.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

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.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

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.