Top AI & Machine Learning Jobs

As AI Research Scientist I, you will propose AI solutions, apply ML models in drug design, publish papers, and collaborate in a fast-paced environment.

The role involves conducting research and developing generative AI technologies, creating prototypes, collaborating with partners, and driving innovation at American Express.

As a Research Engineer, you will optimize AI performance, focusing on kernel development for large-scale ML workloads, profiling bottlenecks, and collaborating with teams to enhance model training and inference.

Part-time 6-month placement for PhD students to apply AI/ML methods to scientific problems: literature review, prototype/model design, validate results with domain experts, document and present findings.

Research and develop machine learning algorithms and software prototypes for sponsor problems. Perform data collection, modeling, fusion, visualization, statistical analysis, and present technical reports. Collaborate with staff, supervise junior personnel and students, and integrate ML prototypes into research and pre-production environments.

Lead original, publication-driven research in foundational AI, focusing on representation learning, reasoning, physics-inspired ML, and AI-quantum/physical science intersections. Own projects end-to-end: problem formulation, math, implementation, experiments, analysis, and publishing; develop reusable research assets and collaborate across disciplines and external partners.

Lead AI safety research for biological applications: design safety evaluations, assess emerging capabilities, partner with scientists to build robust safeguards, inform policy, and communicate results to internal and external stakeholders.

Develop and scale frontier AI systems for biology, including foundation models, RL and multi-agent systems. Design model architectures, pre-training and post-training discovery pipelines, evaluation frameworks, and publish/open-source research with scientific collaborators.

Build and scale frontier AI systems for biology: design model architectures, run large-scale pretraining on GPU clusters, develop RL/reward modeling and multi-agent systems, create evaluation frameworks tied to biological outcomes, and publish/open-source research.

Develop and scale deep learning foundation models and systems for biology. Design model architectures and pre-training pipelines, build post-training discovery systems (RL, reward modeling, reasoning, multi-agent orchestration), create evaluation frameworks tied to biological outcomes, and publish/open-source results while collaborating with scientists worldwide.

Develop and scale frontier AI systems for biology, including foundation models, pre-training pipelines, RL and multi-agent systems, evaluation frameworks, and community-facing publications and open-source releases to accelerate scientific discovery.

Design, build, and operate large-scale ML training and evaluation infrastructure for RL/AI research. Enable distributed, cost-aware GPU training, benchmarking, data curation, and continuous evaluation; collaborate with research and platform teams to productionize models.

Research and develop quantum-accelerated AI algorithms and theory across generative modeling, probabilistic inference, optimization, and scientific machine learning. Design quantum algorithms and resource estimates, build hybrid quantum-classical workflows, prototype systems in Python/JAX/PyTorch and quantum frameworks, and collaborate with hardware teams to inform architecture and benchmarking.

Short-term intensive research internship assisting in dataset collection and preprocessing, model architecture design and tuning, implementation of testing frameworks, analysis of model accuracy and bias, algorithm refinement, and documentation to support foundation-model research.

Short-term, intensive internship supporting foundation model work: prepare datasets, assist with model architecture and testing, analyze outputs for accuracy and bias, refine algorithms, and document findings for research collaboration.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.

Lead development and deployment of bandit models and large-scale neural networks for monetization. Collaborate cross-functionally, drive product and business decisions, mentor engineers, and ensure scalable, high-quality ML solutions across the ML stack (training, fine-tuning, evaluation, deployment, and monitoring).

Design, train, evaluate, deploy, and monitor large-scale ML systems (LLMs and bandits) for Duolingo Monetization. Collaborate cross-functionally, drive product decisions, mentor engineers, and ensure scalable, high-quality solutions.

Undergraduate internship supporting AI/ML research through labeling and annotating geospatial datasets (tracks, images, video, text, sensor data). Collaborate with researchers using labeling tools, contribute to data quality assurance, and support model development for Navy/DoD/IC applications. On-site in State College, PA; up to 20 hours/week during semesters, up to 40 hours/week in summer. U.S. citizenship and background checks required.

Conduct advanced AI/ML research and develop novel models and methods (especially adversarial, safe, or agentic AI). Design, prototype, and integrate ML systems, run GPU-accelerated training, build reproducible experiments, evaluate and benchmark models, and communicate results via reports and publications. Collaborate with interdisciplinary teams and lead technical tasks for mission-focused solutions.

Six-month, on-site AI/ML co-op to develop, deploy, and monitor production ML and time-series forecasting models. Responsibilities include data sourcing/cleaning, building ML pipelines, model evaluation and validation, drift detection, retraining triggers, and maintaining audit-ready production models using ML tooling.

12-week, on-site summer fellowship for MS/PhD students to formulate problems, build simulations, and develop RL/optimization/forecasting models for construction scheduling, capital allocation, and security operations. Conduct rigorous experiments, maintain reproducible code, and collaborate on production paths. U.S. Persons only; no visa sponsorship. Housing and relocation provided for non-local fellows.

Lead and execute independent research programs on NLP and LLM safety, design and run experiments, secure funding, publish and present results, lead cross-functional teams, and develop evaluation tools and challenges to advance digital safety.

Lead research defining fabric's AI shopping roadmap: design and run quantitative studies, build models and benchmarks for AI search (AEO/GEO), run experiments, translate findings into customer-facing insights, and publish research to influence product and go-to-market strategy.

Lead data strategy, architecture, and AI/ML efforts for research and operational goals. Design scalable data pipelines, oversee model development and MLOps, enforce data governance and security, transition prototypes to production, advise stakeholders, and mentor technical teams in regulated biomedical research environments.