Top AI & Machine Learning Jobs

Develops data pipelines, curates and analyzes large datasets, builds quantitative models, validates data and results, and deploys analytics solutions with product teams. Responsibilities include discovering useful datasets, ensuring data quality and reliability, supporting customer-driven requirements, and integrating solutions into interconnected systems. The role requires strong Python and data-handling skills, database expertise, large-scale batch processing experience, and U.S. citizenship.

Build and deploy production machine learning applications, APIs, inference services, and data platforms. Engineer large-scale pipelines using distributed technologies, data modeling, ETL/ELT, transformation, and data quality practices. Translate defense and mission needs into scalable solutions across cloud, containerized, and GPU/CPU environments while collaborating with engineering, data, and mission teams.

Leads architecture, development, and delivery of AI-powered applications and enterprise data platforms. Designs Generative AI solutions using LLMs, agents, RAG, and Vertex AI; develops full-stack cloud-native applications; builds Databricks, Spark, and Lakehouse data architectures; and establishes GCP cloud standards. Provides technical leadership, mentoring, architecture guidance, governance, observability, CI/CD practices, technical evaluations, and technology roadmaps across engineering, data, analytics, and product teams.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

Leads enterprise AI and intelligent automation strategy, including Generative AI, machine learning, and RPA initiatives. Manages a multidisciplinary IT team, budgets, vendors, governance, project portfolios, and end-to-end solution delivery. Partners with business and technology leaders to identify automation opportunities, develop business cases, ensure compliance and security, and deliver scalable solutions with measurable business value. The role is hybrid in Camden, New Jersey, with 15% domestic and international travel.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Build and maintain predictive models, forecasting algorithms, price elasticity frameworks, and revenue management analytics for dynamic pricing and inventory decisions. Develop SQL and Python data pipelines using large-scale enterprise datasets, calibrate revenue management systems, conduct A/B testing and attribution analysis, and create Power BI or Tableau dashboards. Collaborate with revenue managers, IT, data engineers, leadership, and business stakeholders to operationalize models and communicate quantitative insights.

Build backend systems that translate computer vision and machine learning outputs into interactive product experiences. Responsibilities include designing perception-to-product data contracts, shipping AI-generated geometry to a canvas, balancing accuracy with user experience, closing human correction loops, and bridging AI/ML and product engineering teams. The role requires Python and TypeScript, familiarity with PyTorch and model postprocessing, data contract versioning, and experience deploying computer vision or ML outputs in user-facing products.

Supports a natural gas trading desk through daily supply-and-demand reporting, data integrity monitoring, quantitative modeling, back-testing, scenario analysis, and data pipeline automation. The role partners with traders, data scientists, and strategists to identify trading signals, improve reporting tools, analyze market events, and document methodologies. Candidates need quantitative education, SQL and Python skills, commodity-market interest, strong communication, and attention to detail. This is an in-office position in Austin.

Develop, deploy, and maintain machine learning and deep learning algorithms for biosignal and brain data. Lead the full algorithm lifecycle, including requirements, data curation, modeling, validation, production deployment, documentation, testing, and maintenance. Apply DSP and statistical methods, improve internal ML tooling, introduce advanced model architectures, support regulatory-quality processes, present results to stakeholders, and collaborate with clients, clinicians, neuroscientists, and engineers.