Top AI & Machine Learning Jobs

Develop and implement AI safety principles, policies, and operational processes for a mental health product. Investigate user reports and emerging risks, coordinate triage and escalation, translate findings into product improvements, and communicate safety practices internally and publicly. Collaborate with clinical, legal, engineering, research, and machine learning teams while handling sensitive information and making decisions under uncertainty.

Design, develop, test, debug, and maintain scalable software applications and prototypes using Java and Python. Work in a Linux full-stack environment, integrate customer-tailored large language models, design accessible interfaces, connect external requirements tools, troubleshoot performance issues, conduct code reviews, support planning, and mentor junior engineers. The role requires extensive software engineering experience, a bachelor’s degree or equivalent experience, DoD8570 IAT2 compliance, and TS/SCI clearance with polygraph.

The AI Foundry Engineer translates government business requirements into prompts for AI and agent-based systems. Responsibilities include evaluating and improving generated code and architecture, identifying design and coding issues, testing and validating AI-driven applications, and deploying complete systems. The role requires extensive software, cloud, distributed systems, AI, web development, testing, and prompt-engineering experience, along with eligibility for a government Public Trust.

Design, develop, test, and deploy scalable, production-ready computer vision and machine learning solutions for interpreting visual data from images, video, and camera streams. Collaborate remotely with software engineers, data scientists, AI/ML specialists, and other technical stakeholders.

Leads the creation and global scaling of an AI Data Operations practice. Responsibilities include defining service strategy, developing AI data and evaluation solutions, originating and closing client engagements, establishing quality and governance frameworks, building strategic partnerships, providing thought leadership, and enabling scalable managed-service delivery. The role combines AI expertise, consulting, sales, solution design, commercial leadership, and global operations.

Leads marketing science strategy, advanced analytics, applied AI, and marketing automation across agency client accounts. Manages and mentors analysts, advises senior clients, develops statistical and machine learning models, builds measurement and attribution frameworks, optimizes campaigns, and translates complex data into actionable business recommendations. Oversees data governance, privacy, responsible AI, and collaboration across strategy, creative, media, CRM, and technology teams while supporting new business growth.

Lead Avra’s AI research function and scientific strategy for relational foundation models. Manage and mentor research scientists and engineers while designing graph neural networks, representation-learning methods, and reproducible training and evaluation pipelines. Partner with engineering, data, MLOps, and go-to-market teams to deploy models for batch and online inference, establish benchmarks, address concept drift, and deliver measurable enterprise impact in regulated settings.

Intern will identify productivity challenges and process gaps across global sales teams, prioritize improvement opportunities, and build AI-enabled solutions using Microsoft Copilot, agents, chatbots, Excel automation, and related tools. The role also supports sales process documentation, implementation planning, stakeholder collaboration, governance, and measuring efficiency improvements and business impact.

Leads global adoption of QVAC and Tether AI across enterprises, institutions, and governments. Develops go-to-market strategy, identifies AI deployment opportunities, closes strategic partnerships, builds ecosystem relationships, monitors partnership performance, and recruits and leads a global expansion team. Represents Tether in executive, government, and industry engagements while translating client needs into deployable AI solutions with product and engineering teams.

Leads the Scalable Machine Learning team and owns technical strategy for data generation, orchestration, distributed training, and shared ML infrastructure. Responsibilities include managing engineers, setting multi-quarter roadmaps, coordinating GPU scheduling and cloud infrastructure, advancing MLOps practices, influencing peer teams, delivering multi-organization initiatives, and maintaining high reliability across shared systems.