Top AI & Machine Learning Jobs

Supports end-to-end delivery of governed AI solutions across business functions, including generative AI agents, machine learning, computer vision, and optimization applications. Collaborates with data scientists, engineers, product owners, and business stakeholders to prepare data, test and troubleshoot solutions, enforce architecture and coding standards, support Agile delivery, and measure adoption and business value. Helps ensure responsible AI, security, quality, and production readiness across Mosaic’s Azure-based enterprise AI platform.

Leads data science and advanced analytics initiatives for Auto Claims by developing machine learning and predictive modeling algorithms, integrating structured and unstructured data, and creating tools to support business decisions. Communicates modeling results to stakeholders, researches emerging techniques, manages moderately complex projects, and provides mentorship to junior data scientists. The role is remote with up to 10% travel to regional offices.

Own the end-to-end machine learning and LLM lifecycle, taking models and agentic AI workflows from experimentation into reliable, scalable production. Design MLOps infrastructure, serving, orchestration, CI/CD, evaluation, observability, and GPU optimization. Build self-service tooling and establish LLMOps standards while partnering with engineering and business teams. The role is fully remote and involves up to 10% travel.

Own product strategy, requirements, capabilities, and roadmaps for AI-powered document intelligence solutions. Evaluate AI models, platforms, tools, and build-versus-buy options; define evaluation criteria; guide proof-of-concept scope; review benchmarks; and deliver recommendations addressing feasibility, quality, scalability, cost, security, privacy, governance, and production readiness. Partner with engineering, architecture, data science, and security teams while communicating technical tradeoffs to senior stakeholders.

Develop and deploy machine learning systems for robotic welding perception. Responsibilities include point cloud registration, seam detection, joint geometry estimation, 3D data pipelines, synthetic data generation, model evaluation, ROS integration, low-latency inference, and production deployment. Senior and staff engineers additionally lead perception research, sensor fusion, ML lifecycle strategy, distributed training, hyperparameter optimization, and technical mentorship.

Build and productionize AI-driven GTM workflows for Growth, including outbound orchestration, campaign tooling, signal and enrichment pipelines, seller skills, paid-media automation, review agents, and deliverability controls. Own reliability, observability, testing, learning loops, warehouse integrations, and migration to the Firn framework while partnering with data engineers, marketers, sellers, and platform engineering.

Own research and implementation of post-training methods for legal language models, including supervised fine-tuning, reinforcement learning, reward design, verifiers, preference modeling, evaluations, and distributed training. Lead experiments from research direction through production deployment, while debugging training instability, reward hacking, and divergence. Build rigorous evaluation systems and production-quality Python code for long-horizon legal reasoning and agentic workflows.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Develop novel AI and machine learning methods for protein interaction prediction, antibody and minibinder design, affinity prediction, and developability optimization. Design rigorous benchmarks, analyze large experimental datasets, contribute production-quality code and data, deliver models and technical reports, and provide technical leadership and mentorship. Collaborate with wet-lab scientists, external partners, and interdisciplinary teams while communicating research findings through publications, presentations, and scientific discussions.

The faculty member will teach undergraduate and graduate courses, mentor students, develop an extramurally funded research program in physical AI, and contribute to curriculum innovation, shared governance, and professional service. Associate and full professors will also lead major research proposals and center activities. Research areas include multimodal perception, physical dynamics, learning, control, decision-making, robotics, and autonomous systems.