Top AI & Machine Learning Jobs

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.

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.

Lead customer-facing technical programs for frontier AI labs, including reinforcement learning environments, training tasks, evaluation infrastructure, automated rewards, and verification systems. Manage multiple programs, high-volume task production, external technical contributors, cybersecurity and agent safety initiatives, and cross-functional delivery across research, engineering, product, sales, and customers.

Design and build a production-grade AI memory and retrieval engine for personalized, privacy-preserving LLM experiences. Responsibilities include developing multi-tier storage and hybrid retrieval pipelines, memory extraction and classification, adaptive updates, evaluation frameworks, and reliability safeguards. The role researches advanced memory architectures, supports agentic workflows and tool integrations, collaborates with product management, and owns production operations. Strong software engineering, Python, NLP/ML, vector database, LLM orchestration, privacy, and RAG expertise are required.

Develops biomedical data science solutions for the National Library of Medicine, processing and analyzing large-scale clinical and biomedical information. Responsibilities include data cleaning and validation, statistical modeling, predictive analytics, visualization, machine learning, NLP, data integration, and information retrieval. The role collaborates with researchers, developers, and healthcare professionals and may support clinical data studies, research synthesis, and secure handling of healthcare information.

Serve as JFrog’s embedded AI ecosystem strategist in San Francisco, scouting emerging tools and vendors, assessing trends, prototyping APIs and agentic frameworks, and translating ecosystem insights into product strategy and roadmap direction. Build relationships across AI and developer communities, evaluate LLM, RAG, and agentic architectures, brief internal stakeholders, and connect external technical signals with Product, Engineering, and go-to-market teams.

Build scalable AI safety systems for Grok’s multimodal media generation, including real-time moderation, risk detection, safeguards, monitoring, evaluation frameworks, and feedback loops. The role addresses harmful content, bias, deepfakes, intellectual property, and misuse while maintaining performance and low latency. Responsibilities span prototyping through deployment, incident response, and refinement, requiring collaboration with engineering, research, product, and policy teams.