Top Artificial Intelligence Jobs

The Senior Product Manager will prioritize and deliver AI products, support cross-functional teams, enhance key metrics, and drive market leadership through innovation and research.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Lead design, development, and deployment of LLM-powered solutions and autonomous procurement agents on the GEP AI platform. Advise stakeholders, run workshops, configure platform ontologies and data pipelines, optimize models for procurement tasks, and oversee cross-functional implementation to drive automation and improved procurement decision-making.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Build and own backend services for underwriting and AI risk monitoring platforms. Implement end-to-end features across Next.js/React, Node.js/TypeScript, Python and Postgres. Design scalable, observable systems, mentor peers, collaborate with product, data science, and founders, and participate in in-person rapid prototyping and roadmap planning.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Build end-to-end AI systems for clients including LLM integrations, workflow automation, data pipelines, and tooling. Ship production code, iterate on deployed systems, evaluate emerging AI tools, and contribute to internal engineering standards. Work closely with senior engineers and founders on architecture and client outcomes, with opportunity to lead engagements and grow quickly.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Founding role combining strategy and engineering to identify high-ROI AI opportunities with PE portfolio companies, design and deploy end-to-end AI systems (LLM integrations, RAG, agents, data pipelines), lead client engagements and a growing technical team, and own production reliability and outcomes.

Design, build, and deploy prompt-engineered AI agents and copilots for sellers. Create and tune prompts, connect LLMs to GTM systems (Salesforce, Snowflake, Databricks), ship production workflows, run experiments, monitor performance, and drive field adoption while ensuring security, privacy, and responsible AI governance.

Design, build, and operate agentic AI workflows and MCP servers for an LLM-based network operations assistant. Implement LangGraph orchestration, RAG/memory systems, evaluation frameworks, and production integrations (Kubernetes, CI/CD, containers, FastAPI). Collaborate with domain experts, ensure observability, and drive reliability of non-deterministic AI systems at scale.

Design and build a production multi-agent LLM framework and LLM-powered pipelines (text-to-SQL, NLU, structured reasoning). Implement RAG, prompt systems, evaluation and CI/CD for AI features, instrument and optimize model performance, and collaborate with product to deliver user-facing capabilities.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.

Label and outline plants in agricultural images using an online tool, tag crop and weed types per guidelines, maintain high accuracy across many images. Remote, flexible internship (3 months) with a monthly stipend and completion certificate. No coding or prior labeling experience required; video tutorials provided.

PhD-level AI Engineering intern to apply machine learning to large-scale intraday and tick financial data. Responsibilities include alpha generation, portfolio optimization, risk modeling, data preprocessing, and collaboration with researchers to deploy robust ML models for trading.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Design, build, and maintain large-scale AI/ML platform and infrastructure for training, inference, fine-tuning, and Agentic AI. Develop tools, APIs, reliability metrics, and root-cause analyses across application to hardware layers while improving efficiency, resiliency, monitoring, and observability.

Lead and optimize developer ecosystem operations: manage activation calendar, budgets, priorities, and processes. Build AI agents, dashboards, scripts, and automations to reduce manual work, improve reporting and visibility, and drive adoption of new tools and operating rhythms across internal and external teams.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Develop and maintain high-performance C++ machine control and data analysis software for advanced mask inspection systems. Integrate AI/ML capabilities, collaborate with multidisciplinary engineering teams, optimize and scale existing code, and support integration/testing and customer escalations. Work with APIs, containers, and observability tools to enable reliable production deployment.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

Lead forward-deployed engagements with hedge funds to scope and build bespoke AI applications using SuperAnalyst. Drive proof-of-value, define success criteria, build client relationships, run GTM demos, fine-tune models for investment workflows, and translate deployments into product roadmap improvements to increase adoption across accounts.