Top AI & Machine Learning Jobs

Leads the data science roadmap and manages Data Scientists and Analysts while remaining hands-on. Designs data pipelines, ETL/ELT processes, analytical datasets, machine learning models, predictive analytics, optimization, and AI solutions. Partners with executives and cross-functional teams to translate business needs into scalable solutions, establish success metrics, improve data governance, and deliver measurable operational and financial value. Coaches team members, develops organizational capabilities, and communicates analytical outcomes to technical and non-technical stakeholders.

Develops cost transparency, dashboards, forecasting, budgeting, governance, and optimization strategies for AI and machine learning workloads. Partners with engineering, data science, finance, procurement, security, and risk teams to improve cloud and vendor spend, GPU utilization, model deployment efficiency, and responsible AI adoption. Automates cost management using scripting, infrastructure-as-code, and cloud-native tooling while evaluating hosted, internal, and open-source AI platforms.

Supports data science projects by preparing and validating datasets, maintaining metadata, conducting exploratory analysis, developing and validating analytical models, researching data science solutions, and visualizing model insights. Works with team members to provide analysis and recommendations for business decision-making. The role requires a bachelor’s degree in data science, statistics, operations research, computer science, or an equivalent field, and typically targets candidates with 0–2 years of experience.

Leads enterprise strategy, portfolio governance, investment planning, and delivery roadmaps for data products, platforms, artificial intelligence, machine learning, and automation. Oversees responsible AI, platform modernization, data governance, vendor management, budgets, adoption, risk, and measurable business outcomes. Builds and leads multidisciplinary teams through Directors, Senior Managers, and Managers while partnering with business, product, engineering, security, legal, privacy, finance, and operations leaders.

Design and deploy responsible generative AI solutions using LLMs and agentic AI frameworks. Collaborate with MLOps/DevOps, engineers, and business partners to integrate scalable, reliable models into enterprise workflows, build monitoring and ETL pipelines, and communicate results to stakeholders.

The Acquisition Integration Architect will manage client data transformation programs, enhance architecture and AI solutions, and lead cross-functional teams to ensure program execution and delivery.

Leads Anthropic’s societal harms policy design organization, managing teams focused on child safety, well-being, manipulation, election integrity, and related risks. Sets mitigation strategy across model policies, detection and enforcement systems, and product interventions; coordinates decisions across harm areas; prioritizes resources; handles high-severity escalations; and partners with research, engineering, product, legal, data science, external experts, civil society, and regulators.

Lead technical direction for AI infrastructure, data platforms, shared software primitives, and agentic engineering practices. Build scalable, cloud-native services and secure MCP integrations while establishing AI quality gates, evaluation frameworks, responsible-AI controls, SLOs, and architectural standards. Guide modernization, build-versus-buy decisions, and high-stakes platform choices. Mentor senior engineers and influence technical norms across teams while balancing product delivery, partner needs, member trust, security, and operational quality.

Design, develop, and deploy AI solutions for discriminative and generative applications across semiconductor design, manufacturing, sales, planning, and productivity. Build data pipelines, develop LLM agents, train neural networks, test solutions, and monitor model accuracy and drift. Collaborate cross-functionally to deliver secure, scalable, and high-performance systems.

Design, develop, diagnose, prototype, and deploy machine learning, deep learning, computer vision, and image-processing algorithms for semiconductor inspection equipment. The role involves inventing new algorithmic approaches, training and deploying AI models, creating model architectures, modifying loss functions, and collaborating effectively both independently and within a team.