Top AI & Machine Learning Jobs

Supports generative AI application development by evaluating model quality, safety, accuracy, and performance; debugging and optimizing applications; preparing and validating data; and designing secure, scalable solutions with AWS Bedrock and Anthropic Claude. Reviews Python and ReactJS code, documents technical artifacts, tracks tasks, conducts research, and participates in team meetings.

Founding engineer responsible for architecting and building core product components, writing scalable Python code, designing and maintaining AI/ML infrastructure, implementing testing and CI/CD best practices, shipping backend production systems, collaborating on product roadmap, and mentoring future engineers.

Work on production features for an AI-native fleet management platform: write Python/JavaScript/SQL code, design data pipelines and analytics, and gain hands-on experience with web development and AI/ML workflows.

Label and outline plants in agricultural images using an online tool, tag crop and weed types per guidelines, maintain high accuracy across many images. Remote, flexible internship (3 months) with a monthly stipend and completion certificate. No coding or prior labeling experience required; video tutorials provided.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Research Fellows will validate AI-generated paper reproductions, simulations, and results; map research taxonomies; define standards for high-quality AI-assisted science; and create benchmark tasks and evaluations for frontier AI models. Fellows will contribute to a publicly released benchmark paper and open evaluation set as lead or first authors. The contract offers flexible, remote global work for current STEM PhD students or postdocs with published research and hands-on AI agent experience.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Evaluate AI models for dangerous CBRNE-related outputs, designing adversarial prompts, multi-turn attack chains, and threat models. Assess technical accuracy and real-world risk, distinguish open-source information from harmful uplift, score responses using severity rubrics, and document failures. Collaborate with AI researchers and policy teams to improve model safeguards and evaluation frameworks while following strict ethical and operational security protocols.

Develop, deploy, and monitor predictive models and algorithmic decision systems for automated sports pricing and market making. Prepare data, build backtesting and simulation tools, analyze model and trading performance, and improve quoting, pricing, exposure management, and execution quality. Create dashboards and diagnostics while collaborating with traders, quantitative engineers, product, and engineering teams. The role includes occasional evenings, weekends, and holidays aligned with major sporting events.

Set the technical direction for machine learning across underwriting, fraud, risk, and personalization. Own the ML platform and modeling roadmap, including feature stores, pipelines, serving, monitoring, governance, and experimentation. Lead high-impact credit and cash-advance models through deployment and iteration, establish model-risk standards, partner with business and compliance leaders, and mentor engineers. This is a hands-on staff role with company-wide technical influence.