Top Artificial Intelligence Jobs

Partner with teams to design, pilot, and scale generative-AI workflows and automation. Build prompt libraries, evaluate AI platforms, integrate APIs, draft executive communications, and drive cross-functional adoption of AI solutions to improve efficiency and decision-making.

Design, develop, and deploy ML and deep-learning solutions (including generative AI). Lead end-to-end model lifecycle, build RAG/agentic workflows, integrate models into production, collaborate with engineering and clients, monitor performance, and document experiments and data provenance.

Own strategy, design, and optimization of Vanta's customer-facing chatbot and internal AI Copilot. Build and maintain AI-driven knowledge, design conversation flows in Intercom, analyze performance data, and collaborate cross-functionally to increase resolution rates and CSAT. Handle a small volume of support tickets to inform improvements and maintain product expertise.

Lead and manage an onsite Edge AI Applications Lab to design, fabricate, and deploy tiny-ML models on microcontrollers and embedded platforms. Oversee lab projects, hardware prototypes, sensor data collection, model development, and cross-functional/customer collaborations. Mentor junior engineers, manage procurement and scheduling, and optimize real-time edge solutions for consumer, industrial, and automotive applications.

Founding pre-sales Field Engineering role selling STEM datasets, benchmarks, graders, and expert services to frontier AI labs. Lead technical discovery, design solution architectures, build prototypes/demos, run POCs to conversion, interface between GTM and R&D, and codify playbooks and demo libraries to scale STEM opportunities.

Lead and deliver GenAI projects: design and architect multi-agent LLM systems, build training/fine-tuning and deployment pipelines (RAG), own technical roadmaps, mentor engineers, implement evaluation and observability pipelines, and integrate solutions on cloud platforms to meet enterprise requirements.

Own and optimize on-board inference architecture for humanoid robots: map and partition models across heterogeneous accelerators, enforce compute budgets, optimize toolchains and kernels, apply quantization/pruning, profile pipelines, and collaborate with AI, platform, and silicon vendors to meet real-time power, latency, and reliability constraints.

Lead hands-on design and build of secure AI/ML solutions for federal agencies: engineer LLM orchestrations, RAG pipelines, vector DBs, hybrid cloud/on-prem integrations, DevSecOps, compliance (FedRAMP/NIST), ATO support, and executive technical advisement.

Design, build, and optimize AI-driven customer success automations using no-code/low-code tools and LLM APIs. Identify use cases across the customer lifecycle, integrate solutions with enterprise platforms, apply prompt engineering and RAG patterns, monitor performance, and collaborate cross-functionally to scale AI-enabled workflows while maintaining responsible AI practices.

Develop and advance state-of-the-art deep learning methods for protein and antibody design. Lead projects on generative and foundation models, implement large-scale distributed training and GPU-based inference, collaborate with biology and drug discovery experts, publish and communicate research, and contribute to open-source efforts.

Senior technical leader responsible for the success of complex enterprise AI deployments: setting architecture and evaluation strategy, ensuring production reliability, coaching and developing engineering leads, partnering with customer executives, debugging critical issues, and scaling pilots to enterprise deployments while using AI-native engineering practices.

Design, develop, and deploy end-to-end ML/AI solutions (RAG, retrieval, embeddings, knowledge graphs) on cloud platforms. Build ingestion pipelines, implement secure self-hosted models, ensure PHI/PII compliance, advise clients, mentor team members, and deliver measurable outcomes.

Design, build, deploy, and operate production AI systems for customers. Implement end-to-end AI workflows (models, prompts, agents, retrieval, evaluators), write maintainable Python services, monitor and debug live systems, create evaluation and observability tooling, and work closely with stakeholders to iterate systems for reliability and business value.

Lead technical delivery and architecture of production AI systems for enterprise customers. Mentor engineers, integrate models/agents with customer data and APIs, operate and improve live systems, measure and debug failures, and drive pragmatic, reliable deployments that deliver business outcomes.

Design and build scalable backend services and data ingestion pipelines to process and enrich data (including LLM enrichment). Perform data modeling, mapping, and large-scale file manipulation while collaborating in an Agile team to deliver reusable APIs, optimize performance, and improve processes.

Lead design and continuous improvement of digital quality systems across Supply Chain Technology. Oversee system validation, testing, compliance, risk management, and AI-driven analytics to monitor and optimize performance. Manage a team, partner with cross-functional stakeholders, and drive process improvements using Lean/Six Sigma in a regulated MedTech environment.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

Lead enterprise AI Quality Engineering strategy and operations, creating validation frameworks, runtime assurance, synthetic test data and simulation platforms, and governance-aligned quality practices. Partner across engineering, clinical, security, data, and governance teams to ensure secure, compliant, observable, and scalable AI deployments in healthcare while driving organizational transformation, tooling evaluation, and team development.

Design and build full-stack AI prototypes; decompose products into model, retrieval, prompting, agents, and orchestration components; integrate APIs, databases, and cloud; define evaluation loops; perform error analysis; collaborate cross-functionally; and present results to founders and partners.

Own the full customer lifecycle for B2B freight customers: run pilots and implementations, drive adoption and renewals, collaborate with engineering on integrations, build 0-to-1 CS processes and documentation, and deliver customer education to maximize value from voice-agent platform.

Provide deep technical support for Devin and Windsurf by reproducing, diagnosing, and resolving complex customer issues across codebases, CI/CD, containers, cloud platforms, and developer tooling. Perform root-cause analysis, escalate with full technical context, create playbooks and tooling, and support release testing to improve reliability and adoption.

Design, build, and ship enterprise-grade AI agents and LLM-backed workflows for legal workflows. Implement retrieval, memory, evaluation, benchmarking, and feedback loops; work with production systems, data collection, and cross-functional teams to improve platform performance and compliance.

Customer-facing presales leader who partners with federal stakeholders to identify and scope high-value AI/ML use cases, develop business cases and ROI analyses, align customer priorities with C3 AI capabilities, scope pilot engagements, and support sales and product teams to capture and expand federal accounts.

Lead QA for Data & AI features: design and run test strategies for ML/LLM outputs, write data validation in Python/SQL, implement Databricks pipeline checks, automate tests (Katalon/Playwright), validate APIs/ETL, monitor model performance and data drift, and collaborate across Engineering, Product, and Data to define acceptance thresholds and CI/CD quality gates.

Lead AI value-creation for strategic PE-backed and franchise customers by quantifying ROI/EBITDA impact, closing performance gaps across people/process/technology, driving expansion of AI products and locations, directing on-account execution teams, and translating frontline results into board-level reporting while feeding learnings back to Product and Engineering.