Top AI & Machine Learning Jobs

Leads enterprise AI strategy, consulting, solution design, development, integration, automation, governance, and adoption. Partners with business and technology leaders to identify opportunities, build agentic AI and generative AI solutions, integrate AI with enterprise systems, optimize workflows, and establish secure, scalable, responsible AI practices. The role includes technical leadership, discovery workshops, process assessments, stakeholder advising, platform evaluation, and oversight of AI initiatives across the organization.

Leads the design, development, deployment, and architecture of scalable AI-powered applications within the Alli platform. Manages and mentors engineers, drives coding and testing best practices, conducts code reviews, and collaborates with product and client teams. The role requires hands-on software development across frontend and backend technologies, cloud deployment, generative AI and LLM integration, system architecture, CI/CD, and containerization while providing technical guidance and strategic insights.

Supports data projects across digital marketing, products, business analytics, and student lending. Uses SQL and Python to prepare and analyze data, develop pipelines and dashboards, contribute to predictive models and AI enablement, investigate data quality, document findings, and communicate results to nontechnical stakeholders. This is a 12-week hybrid internship based in Newark, Delaware, or Arlington, Virginia.

Lead the design, optimization, and production deployment of high-performance LLM inference systems using vLLM, CUDA, Triton, C++, and Python. Improve GPU utilization, latency, throughput, scalability, and cost through kernel optimization, quantization, batching, memory management, distributed inference, and profiling. Contribute to upstream open-source projects, establish engineering standards, review performance-critical code, and guide research-driven serving improvements.

Data Scientist Intern supporting Syngenta’s Vegetables R&D team by processing large structured and unstructured datasets, developing and validating machine learning models for product placement predictions, integrating varied data types, and documenting findings and recommendations. The role involves collaborating with data scientists on research initiatives and applying AI/ML methods to genetic, phenotypic trial, and environmental agriculture data.

Develops computer vision and machine learning perception algorithms for autonomous space-threat detection. Responsibilities include training, evaluating, optimizing, and deploying models; predicting relative pose of dynamic targets; integrating perception into simulations and real-time autonomy pipelines; creating sensor models and digital twins; and maintaining model-training and evaluation infrastructure.

Build and scale enterprise AI tooling for software development, including agent runtimes, evaluation, guardrails, monitoring, and model-portable workflows. Lead AI service and developer-tool design from concept through production while ensuring reliability, security, privacy, auditability, performance, and cost efficiency. Collaborate with engineering, DevEx, IT, Security, Compliance, and Audit teams; measure developer impact; influence technical direction; and mentor engineers across levels.

Leads the US agentic AI consulting business, advising C-level clients, shaping AI ecosystems, developing ROI cases, overseeing delivery and governance, winning multimillion-dollar programs, and building a high-performing AI consulting team. The role combines strategic advisory, solution architecture, portfolio oversight, responsible AI governance, thought leadership, and commercial growth across marketing, CX, and technology engagements.

Develop advanced Bayesian models and stochastic simulations for business modeling, product innovation, user behavior, and system performance. Manage analytical projects end to end, including data preparation, model development, validation, deployment, and proof-of-concept applications. Communicate methodologies, progress, and findings through reports and stakeholder presentations.

Conduct applied machine learning research focused on language and multimodal models. Develop and evaluate novel methods for language understanding, generation, reasoning, adaptation, and continual learning. Build and optimize LLM and VLM systems using Python and deep learning frameworks, including distributed training. Communicate quantitative research findings to technical audiences and contribute through publications, patents, open-source work, or deployed systems.