Top Artificial Intelligence Jobs

Identify and qualify new nonprofit partnership opportunities, conduct outreach via calls, email and social, collaborate with Account Executives, refine outreach playbooks, and support early-stage sales pipeline development.

The Principal Systems Engineer will provide systems engineering and project management for AI/ML architectures, coordinate program management activities, and improve processes within an agile development environment.

The Staff MLOps Engineer will own the AI/ML platform, focusing on the Synthetic Data Platform, overseeing the ML lifecycle, building infrastructure, and mentoring engineering teams while ensuring cost-effectiveness and system performance.

Manage full sales cycle to grow nonprofit customers, build relationships with decision-makers, close deals, expand accounts, share market insights, and hit revenue targets.

Manage back-end operations of an AI-powered autonomous fundraising system, including human-in-the-loop review, AI training, knowledgebase development, documentation, onboarding, basic website/content support, third-party platform coordination, JIRA issue reporting, and customer troubleshooting.

Manage full sales cycle to acquire and grow nonprofit customers, build relationships with decision-makers, close SaaS deals, share market feedback, and hit revenue targets while supporting existing accounts.

Lead design and delivery of production LLM systems for drug discovery: build RAG pipelines, hybrid retrieval, text-to-SQL, agentic workflows, evaluation frameworks, and orchestration for scientific analyses.

Manage a book of nonprofit customers to drive adoption, retention, renewals, and upsell. Proactively communicate, document engagements, oversee onboarding, analyze usage, execute renewal strategies, and collaborate cross-functionally to improve processes and customer outcomes.

The Lead Global Solutions Architect designs and supports enterprise technology solutions, collaborating with teams to deliver scalable and secure architectures for both cloud and on-premises environments.

As a Technical Mentor at Udacity, you will provide personalized support through video calls, conduct technical deep-dive sessions, and host group Q&A sessions to assist learners in AI and related fields.

Lead architecture and implementation of a Robot Brain combining vision-language models, visuomotor policies, and world models. Ship a production Robot Brain on physical hardware, define evaluation and safety protocols, integrate with cross-functional teams, and hire and mentor a small embodied AI team.

As a Senior Engineer, you'll design and build AI-powered software and production systems, focusing on LLMs and performance optimization, while collaborating with a multidisciplinary team to drive innovation and responsible AI governance.

As a Senior Software Engineer on the AI Developer Experience Team, you'll build and maintain developer tools, implement workflows, and collaborate with various teams to enhance internal engineering processes.

The Senior Product Manager will lead the roadmap for AI-based shopping experiences, collaborating with engineering, design, and data teams to create user-friendly AI interactions and improve product engagement metrics.

The Principal Engineer will lead the design and development of technical solutions, oversee project teams, and maintain existing solutions, focusing on APIs and innovative technology applications.

The Senior Product Designer will enhance user experiences for The Washington Post's digital products by leading human interaction design initiatives, focusing on accessibility and personalized journalism delivery. Responsibilities include prototyping, defining UI patterns, and collaborating with various teams to ensure design meets high journalistic standards.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

Own AI product strategy and roadmap across clinician- and member-facing surfaces. Partner with engineering, applied ML, design, and clinical teams to ship, scale, and measure LLM/ML-powered features; set safety and quality guardrails, run evals, make model build-vs-buy decisions, and translate ML capability into clinical workflow outcomes.

Lead AI initiatives for quantum system operations: define roadmap, prioritize bounded-AI pilots for calibration and readout, ensure model validation, observability, safety gates, and cross-functional execution to reduce calibration time, failed jobs, and recovery time.

The AI Deployment Engineer will build, deploy, and scale AI solutions while collaborating across departments to ensure successful implementation and support of operational workflows.

Drive architecture and implementation of Databricks AI Runtime for large-scale GPU training. Build scheduling, distributed training performance, resilience, observability, and developer-facing APIs/CLI. Improve GPU utilization, fault tolerance, and multi-node orchestration. Lead cross-team initiatives, mentor engineers, and deliver high-impact platform features supporting thousands of accelerators and long-running jobs.

Lead design, implementation, and long-term strategy for cloud platforms (public/private/hybrid). Mentor teams, create best practices, drive orchestration/automation, provide Tier 3 support and root-cause analysis, and coordinate cross-team cloud initiatives.

Design, build, and scale Databricks AI Runtime for large-scale GPU training. Lead architecture and implementation of multi-node orchestration, scheduling, checkpointing, resilience, throughput optimization, observability, and developer tooling. Mentor engineers, partner with product and research, and deliver high-impact production rollouts supporting thousands of accelerators and diverse model workloads.

Lead architecture and delivery of a neurosymbolic AI platform blending LLMs with constraint-based planning. Set AI strategy and governance, evaluate and integrate state-of-the-art models and vector infrastructure, resolve complex AI engineering roadblocks, and produce frameworks to enable large-scale, enterprise-grade autonomous planning.

Drive adoption of Stripe payment and monetization solutions for AI companies by architecting custom billing strategies, influencing product roadmap, engaging technical founders, supporting implementations, and leading cross-functional efforts to position Stripe as the AI monetization partner.