Top AI & Machine Learning Jobs

Review and annotate video assets according to project guidelines, maintain high accuracy and consistency, identify footage quality issues, and report edge cases or contradictions to the AI team. The role also contributes feedback on questionnaire design and process improvements during extended review sessions. This is a part-time, 10-week onsite contract in San Francisco, with possible extension.

Develop and deploy complex machine learning and analytics models for client systems. Manage large datasets, perform data ingestion, cleaning, feature extraction, modeling, validation, visualization, and reporting. Collaborate with clients and distributed data science and systems engineering teams while delivering maintainable, tested, version-controlled, cloud-based solutions within cost and schedule constraints.

Develop and prototype computer vision and machine learning solutions for robotic trailer unloading. Train and adapt models for perception tasks such as object detection, segmentation, grasp estimation, depth estimation, and anomaly detection. Troubleshoot real robotic systems, evaluate emerging robotics advancements, transition prototypes to product teams, communicate technical priorities, mentor junior engineers, and provide technical leadership on key projects.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.

Develop and deploy production-grade machine learning models, manage model lifecycles through MLOps practices, and support ML platforms and operations. The role requires strong Python, PyTorch, TensorFlow, Azure, software engineering, and coding expertise. Preferred experience includes generative AI, large language models, retrieval-augmented generation, vector databases, and semiconductor or manufacturing domains.

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.

Paid internship for quantitative students working with AI Research, Strategic Finance, or the Office of the CEO. Interns will tackle AI-native software creation problems, prototype mathematically rigorous solutions, collaborate cross-functionally, and ship tools or infrastructure impacting millions of developers.

Leads iSpot’s AI engineering roadmap and team, taking generative AI, agentic workflows, retrieval systems, and machine learning capabilities from prototype to reliable production. Owns AI architecture, evaluation, guardrails, cost, latency, and operational performance while partnering with product, data science, and platform teams. The role combines hands-on coding and experimentation with hiring, technical leadership, roadmap planning, responsible AI, and executive communication.

Build and deploy AI agents for finance customers by configuring workflows, tuning prompts and models, and integrating enterprise finance systems. Partner with finance teams to identify high-value use cases, improve onboarding and customer journeys, create usage analytics and health frameworks, and inform go-to-market and expansion strategies. The role requires strong finance and accounting knowledge, SQL proficiency, comfort with customer data and BI tools, and the ability to work independently in a fast-paced startup environment.

Conduct applied research on foundation models for real-time fraud detection using large-scale behavioral and sequential data. Design experiments, benchmarks, holdouts, and monitoring systems; develop models from data preparation through deployment; and optimize training and inference efficiency. Collaborate with engineering, client-facing teams, legal, compliance, and customers to productionize models and establish explainability, governance, and risk documentation.