Top Machine Learning Jobs in Baltimore, MD

The Senior Director leads research in Application Platforms & AI, guiding clients in AI strategy and market trends, while producing critical research content and engaging with clients directly.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

Join Invoca's Talent Community to stay updated on job openings and to explore exciting career opportunities across various departments.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Lead vision, strategy, and roadmap for Fetch's referral growth engine. Own end-to-end referral lifecycle, build incentives, experimentation, AI-driven personalization, and cross-functional initiatives to drive MAU growth and durable network effects.

Manage a portfolio of SaaS customers, build relationships, monitor usage, prevent churn, handle support and troubleshooting, identify upsell/renewal opportunities, and relay customer feedback to product and engineering.

Senior product engineer owning end-to-end product surfaces for a computer vision platform. Ship features from idea to production, prioritize roadmap, design UX details, integrate APIs, improve annotation and model-assisted labeling, and collaborate with engineering and users to drive product impact.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.

The Solutions Engineer will provide technical support for customers, ensure satisfaction, communicate with sales, and analyze customer needs for product enhancements.

As the Engineering Lead, you will guide product strategy, manage and mentor engineers, and ensure successful execution in product areas, focusing on machine learning and technical expertise across teams.

The Product Manager will lead product development, working with cross-functional teams, driving growth, and creating user experience frameworks.

The Hyperscalers and Commercial Lead will develop technology-driven business strategies, drive customer adoption, and manage end-to-end sales processes while working closely with cross-functional teams.

Design, build, and operate production AI platform capabilities and agentic workflows. Collaborate on architecture, write production-quality backend code, mentor developers, troubleshoot production issues, and drive adoption of reusable AI frameworks, MLOps practices, and cloud-native intelligent services across a multi-tenant SaaS dental platform.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

Lead pre-sales technical engagements for Conversational and Generative AI solutions, collaborate across teams, and design value-driven demos for enterprise customers.

The Principal Architect will lead AI/ML solutions, mentor teams, and collaborate with clients to design and deliver scalable systems using Google Cloud technologies, ensuring compliance with ethical standards.

The Chief Architect will lead the technical direction for Legion's Workforce Management platform, focusing on architecture decisions, AI systems, and scalable software solutions. Responsibilities include hands-on coding, technical leadership, and ensuring systems meet enterprise standards for performance and security.

The Data Science Manager will lead and mentor a team of data scientists, drive data-driven decision-making across Stripe, and collaborate with various stakeholders. Responsibilities include managing projects, recruiting, and contributing to broader data science initiatives.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

Develop and deploy scalable analytics and tools with data scientists and mission analysts. Perform end-to-end engineering: prototype, develop, integrate, test, and deploy analytic solutions into the customer's architecture to operationalize data science.

Design and implement analytic methods to extract meaning from large, messy NSA/CSS datasets. Build and evaluate models, manage and curate data, translate mission needs into technical requirements, and communicate technical conclusions to non-technical stakeholders while accounting for collection and processing constraints.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.