Top AI & Machine Learning Jobs

Develop and manage production-grade machine learning models for wholesale power and gas markets, including price forecasting, congestion analysis, load forecasting, and trading algorithms. Own the ML lifecycle from feature engineering and model selection through deployment, monitoring, and refinement. Analyze ERCOT and PJM market drivers, build data visualizations, apply LLMs, collaborate with technical and business stakeholders, and communicate insights that support trading, risk mitigation, and operational decisions.

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

Develop machine learning models to predict printability of complex CAD geometries using image, thermal, simulation, and sensor data. Design and maintain structured and unstructured data systems supporting diverse data ingestion, storage, and retrieval. Deploy models into production in collaboration with preprint, process, and applications teams. Experience with geometric data, production software engineering, and Python or C++ is required; powder bed fusion and manufacturing data workflows are preferred.

Build Town’s greenfield AI context and retrieval infrastructure, including hybrid lexical and semantic search, durable data models, realtime and batch pipelines, indexing, and storage. Optimize latency, cost, freshness, completeness, and reliability at scale. Establish the foundation for a knowledge graph connecting people, companies, projects, and content, while taking systems from prototype through production.

Build and deploy automation workflows, AI-enabled tools, and production-ready applications using LLM APIs and agentic frameworks. Translate business processes into scalable technical solutions, integrate enterprise systems and SaaS platforms, and collaborate with operators, analysts, leadership, and central IT. Provide training and documentation, support security and governance reviews, and contribute to shared AI development standards across business units.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Develops and maintains data-driven tools for Diplomatic Security risk and threat assessments. Collects, integrates, analyzes, and securely manages data throughout its lifecycle; builds hardware and software solutions; analyzes alarm and operational data; improves workflows and procedures; manages system configuration and releases; delivers executive briefings; and supports interagency coordination involving classified national security information.

Own end-to-end computer vision and machine learning projects for power grid infrastructure, from problem framing and research experimentation through production deployment and monitoring. Develop detection, segmentation, classification, anomaly detection, and foundation-model solutions; build data pipelines, serving systems, experiment tracking, and model versioning. Conduct error analysis, benchmarking, tuning, code reviews, and testing while translating client requirements into reliable production models and communicating technical decisions and limitations.

Develop and execute a firm-wide AI strategy, identify AI and automation opportunities, evaluate platforms and vendors, lead implementation, establish governance and responsible-use practices, drive adoption among attorneys and business teams, measure ROI, and advise leadership on emerging capabilities and risks.

Design, build, and scale AI engines, conversational assistants, context-aware agents, and workflow automations for go-to-market teams. Own the GTM AI roadmap, trusted data and knowledge architecture, AI governance, production monitoring, adoption, and value realization. Partner with Sales, Marketing, Revenue Operations, Product, and Systems leaders to translate business needs into scalable AI solutions and measure their impact.