Top AI & Machine Learning Jobs

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Build production applications and integrations powered by multimodal AI models. Responsibilities include designing scalable architectures, developer SDKs, evaluation and benchmarking infrastructure, reference implementations, and high-volume data pipelines. The role also involves debugging across application and inference stacks, partnering with enterprise adopters, guiding proofs of concept, and translating real-world requirements into model and platform roadmap improvements.

Build and deploy production-grade AI applications for enterprise clients. Translate business requirements into solutions using Python, LLMs, RAG, AI agents, LangChain or similar frameworks, vector retrieval, and modern web technologies. Develop backend services and user-facing applications, integrate enterprise APIs and IAM systems, deploy with cloud platforms, Docker, and CI/CD, and optimize application quality, security, latency, reliability, and business performance. The role is full-time, remote, client-facing, and requires strong stakeholder communication.

Build and deploy real-time computer vision and perception systems for autonomous aerial platforms. Responsibilities include camera integration, calibration, sensor synchronization, algorithm development, embedded optimization, performance evaluation, model retraining, and field testing. The role requires taking perception systems from bench development through flight testing, analyzing real-world logs, and improving detection, tracking, and state-estimation performance under constrained hardware and uncontrolled operating conditions.

Supports analytics projects by understanding datasets, developing models, visualizations, reports, and actionable business insights. Collaborates with analytics teams, applies analytical tools to large datasets, adapts to changing business needs, and develops technical and leadership skills through a 12-week summer internship.

Develop NLP and GenAI solutions for text classification, sentiment analysis, entity recognition, translation, and semantic analysis. Responsibilities include text preprocessing, feature engineering, model training and evaluation, exploratory data analysis, algorithm optimization, distributed processing, and collaboration with engineering and domain teams. The role also requires documenting methodologies and staying current with NLP and machine learning advancements.

Conduct an applied AI research project with mentorship, covering agent evaluation, reasoning, tool use, policy adherence, post-training, and multimodal or voice models. Design rigorous experiments, analyze proprietary text and audio datasets, develop benchmarks, write and submit research papers, and collaborate with engineers to translate findings into production improvements.

Lead and mentor an AI agent engineering team developing scalable, secure HR and Finance AI solutions. Drive technical strategy, project delivery, platform integration, and adoption of LLMs, RAG, and orchestration frameworks such as LangGraph. Partner with product, data science, architecture, vendors, and open-source communities while ensuring privacy, security, compliance, testing, monitoring, reliability, and performance.

Summer 2027 data science internship supporting Northrop Grumman’s Product Support organization. The intern will develop machine learning and predictive analytics solutions, build statistical tools, model complex data, write maintainable code, contribute to testing and documentation, implement CI/CD practices, and optimize algorithms and data workflows in an on-premise environment.

Analyze neural and behavioral research data end to end, including statistical design, signal quality, preprocessing, quality control, decoding models, pose and kinematic analysis, and reproducible pipelines. The role supports study design, closed-loop sessions, publications, and collaboration with scientific and software teams to translate research methods into reliable workflows.