Top Artificial Intelligence Jobs

The AI Engineer IV will lead the development and implementation of AI systems, influence architecture decisions, mentor team members, and ensure best practices in a hybrid work environment.

Design and maintain scalable data pipelines and ETL/ELT workflows; build and operate training, deployment, and model-serving infrastructure; develop feature stores and vector databases; ensure reliability, low latency, and observability; collaborate with Research/Data Science to productionize models; contribute to infrastructure-as-code and cloud documentation.

As an AI Engineer at Clarium, you will build and maintain data enrichment pipelines, design classification workflows, analyze datasets, and ensure data quality, primarily using Python and SQL, and work closely with senior engineers and data scientists.

The Solutions Architect will engage with strategic customers, design end-to-end solutions for AI-driven engineering workflows, and provide guidance on adoption strategies while collaborating with account teams and product stakeholders.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

The Application Engineer will develop AI Physics capabilities, work with customers to tailor solutions, and enhance product features based on customer feedback. Responsibilities include building AI-ready data pipelines and debugging complex issues in workflows.

The Senior Backend Developer will architect and build scalable backend systems, integrate AI technologies, and collaborate with frontend teams to enhance user experience.

As a Junior Backend Developer, you'll translate UI/UX designs into code, integrate with APIs, and utilize AI tools for efficiency.

The AI Engineer will develop LLM-powered healthcare applications, improve documentation platforms, build inference pipelines, and collaborate with teams to launch features, all aimed at reducing clinician burnout.

Full-lifecycle technical recruiter owning searches for ML, robotics, systems, and infrastructure roles. Source passive top-tier candidates, design interview processes, advise on compensation and closing strategies, partner with founders and hiring managers, build market maps and dashboards, and steward candidate experience with data-driven sourcing and hiring approaches.

As an AI Researcher, you will optimize inference performance for large-scale machine learning models by improving latency, throughput, and cost efficiency through various model and systems-level optimizations.

The AI Researcher will develop training optimization techniques, improve large model training efficiency, run experiments and publish research findings.

The AI Researcher will develop and evaluate model distillation techniques, analyze trade-offs, run experiments, publish research, and collaborate for production deployment.

The Operations Specialist ensures smooth operational delivery for partner medical practices, improving workflows and collaborating across functions to maintain data integrity and quality.

The AI Researcher will design and execute research on multilingual datasets, improve cross-lingual transfer in language models, and publish findings at top conferences while translating research into production improvements.

Owns execution and operationalization of enterprise AI strategy into scalable solutions. Leads portfolio delivery, stakeholder alignment, governance, responsible AI practices, adoption/change, and cross-functional teams to drive measurable business value.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Lead end-to-end delivery of enterprise AI/ML and GenAI programs, managing POC-to-production deployments, global engineering and data teams, stakeholder governance, KPIs, budgets, and timely, cost-effective delivery.

Design realistic, specialty-specific clinical case scenarios and gold-standard attending-level responses, evaluate and grade AI-generated outputs using structured rubrics, provide detailed written feedback to improve model clinical reasoning, and participate in onboarding, calibration, and review sessions in a fully remote, asynchronous contractor role (~20 hours/week).

Lead end-to-end delivery of enterprise AI/ML and GenAI programs from POC to production. Manage global engineering and data teams, LLM applications (RAG, agents, copilots), data science deployments, and AI-driven automation. Drive program governance, KPIs, budgets, stakeholder engagement, and ensure timely, cost-efficient, high-quality delivery.