Top AI & Machine Learning Jobs

Lead development of complex data pipelines, AI/ML systems, and software solutions. Provide technical leadership, mentor engineers, conduct code reviews, make architectural decisions, and collaborate with technical and non-technical stakeholders. Maintain hands-on Python development using scalable data engineering practices, cloud platforms, containerization, CI/CD, and machine learning frameworks. Experience with large language models and AI agents is highly desirable.

Conduct data science and Multi-INT analytics across complex intelligence datasets. Develop data pipelines, statistical analyses, predictive and machine learning models, AI-enabled applications, dashboards, and visualizations using R, Python, SQL, Power BI, and Tableau. Integrate structured and unstructured data, evaluate model performance, identify trends and anomalies, and communicate analytical findings to technical and mission stakeholders. The position requires a TS/SCI clearance and may involve deployment for up to six months annually.

Leads product strategy, roadmap, and business outcomes for Tubi’s discovery, recommendations, ranking, search, personalization, and AI experiences. Partners with engineering, machine learning, and data science teams on advanced recommendation systems, deep learning, foundation models, experimentation, and content understanding. Evaluates emerging AI technologies, drives viewer engagement and retention, informs content acquisition decisions, and manages a high-performing product team.

Build and deploy AI-enabled automation solutions across enterprise systems. Partner with Business Applications, Data & Analytics, and IT teams to analyze processes, identify automation opportunities, integrate platforms, test and monitor solutions, document implementations, and train process owners. The role requires hands-on coding, AI workflow experience, enterprise integrations, scripting, SQL, structured data knowledge, production support, and strong communication with technical and non-technical stakeholders.

Leads and develops a perception and machine learning engineering team building computer vision, sensor fusion, tracking, detection, and scene-understanding capabilities for autonomous systems. Owns roadmap planning, technical decisions, delivery, model development and deployment workflows, field readiness, hiring, performance management, and cross-functional execution with autonomy, embedded, data, hardware, product, programs, and field operations teams.

Develops and maintains AI governance policies, frameworks, and controls aligned with regulatory and industry standards. Conducts AI model risk assessments covering bias, explainability, data quality, and security; evaluates organizational and vendor risks; supports audits; designs controls; and partners with Legal, Privacy, Security, and Technology teams. The role also promotes compliant AI practices and organizational awareness while applying advanced AI/ML knowledge.

Develops and implements AI-driven solutions by creating sophisticated prompts and agents, aligning initiatives with digital transformation strategy, and integrating AI into business workflows. Collaborates with technical and business teams to translate needs into AI use cases. Leads quality assurance, volume, bias, and ethical testing while ensuring scalability, reliability, compliance, and repeatable implementation across the organization.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Leads end-to-end AI enablement consulting engagements, including readiness assessments, governance frameworks, champion programs, workshops, use-case discovery, adoption change management, and training. Develops executive-ready strategies, roadmaps, risk models, dashboards, and other client deliverables. Advises senior AI leaders, mentors junior staff, supports AI solution deployment, evaluates emerging tools, and contributes to business development and revenue growth.

Build and integrate machine learning systems using real-world sensor, document, and operational data. Responsibilities include developing Python data pipelines, training and evaluating models, integrating models or LLM workflows into tools, investigating failure modes, documenting results, and using version control, testing, code review, and experiment tracking. The intern will own a scoped project and present findings and recommendations at the end of the summer.