Top AI & Machine Learning Jobs

Lead the enterprise AI platform and agent ecosystem, including multi-agent orchestration, reusable agent templates, self-service RAG, tool and skill registries, prompt management, evaluation frameworks, guardrails, and human-in-the-loop workflows. Partner with cybersecurity, architecture, governance, and business teams to deliver secure, scalable, compliant AI solutions. Drive adoption through low-code tooling, mentor engineers, and shape enterprise AI strategy across commercial, medical, R&D, and corporate functions.

Supports R&D teams developing computer vision and machine learning algorithms for UAV-mounted cameras and robotic platforms. Responsibilities include data preparation, model development, testing, visualization, performance evaluation, error analysis, and deployment strategy research. The intern will write and test software, conduct experiments, analyze datasets, and collaborate on focused design teams in an onsite aerospace and defense environment.

Lead end-to-end dynamic pricing models using demand modeling, causal inference, market simulation, and optimization. Develop pricing approaches for elasticity, seasonality, pacing, and lead-time dynamics; design experiments measuring marketplace effects; and translate complex business problems into deployed solutions. Own production Python code, model deployment, monitoring, retraining, reproducibility, and ML operations while influencing product strategy and collaborating with technical and executive stakeholders.

Research and develop computer vision, sensor fusion, pose estimation, and machine learning solutions for imaging and embedded applications. Build prototypes and end-to-end pipelines in Python and C++, optimize models for real-time edge deployment, and implement MLOps practices for monitoring, versioning, and CI/CD. Evaluate novel algorithms against current research, develop preprocessing and augmentation workflows, and transition validated concepts toward production.

Design, build, and deploy AI/ML and generative AI solutions for internal teams and biopharma clients. Integrate AI APIs, create prototypes and production applications, develop data pipelines, and deploy scalable cloud-based systems. Build client-facing tools, dashboards, and intelligent services while evaluating emerging AI technologies. Collaborate with cross-functional teams, provide technical guidance, gather requirements, support platform rollouts, and promote responsible AI, LLM, RAG, prompt engineering, and agent best practices.

Researches and develops image, color-processing, and computer-vision algorithms for digital camera systems. Expands ISP and computer-vision functionality, quality, and performance; supports hardware implementation and validation; tunes and measures image quality; and documents algorithms. Requires advanced electrical engineering or computer science education, 10+ years of experience, camera ISP or computer-vision expertise, knowledge of colorimetry and image sensing, and strong C/C++ and MATLAB skills.

Design and deploy generative AI workflows and multi-agent architectures for healthcare applications. Curate EHR and claims data, extract clinical variables from unstructured medical text, and develop evaluation pipelines for agent safety, accuracy, factuality, and compliance. Apply advanced machine learning, NLP, and LLM techniques to create trustworthy longitudinal datasets supporting autonomous care agents and clinical decision tools. Communicate technical findings to technical and non-technical stakeholders.

Lead the architecture and technical strategy of Stord’s AI platform while building agent orchestration, tool systems, prompt management, integrations, and stateful edge services. Ship reliable LLM-powered features, guide AI-assisted development, establish engineering quality standards, mentor engineers, manage technical risks, and collaborate with product, data science, ML, and engineering teams.

Build and extend Kubernetes orchestration for AI inference: implement CRDs/controllers in Go, design high-performance ingress/load-balancing, implement GPU-aware autoscaling (HPA/VPA), tune scheduler/resource management, and ensure HA for platform components.

Lead the design, development, deployment, and maintenance of machine learning models for personalization, recommendations, churn prediction, lifetime value, advertising, and business insights. Own the full data science lifecycle, analyze complex datasets, collaborate with product, engineering, editorial, and revenue teams, mentor junior staff, and promote advanced data science practices.