Top Machine Learning Jobs in Baltimore, MD

Analyze and assess malware threats for USCYBERCOM, leading forensic analysis, developing technical reports, and collaborating on threat detection and response.

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.

Lead development and analysis of advanced financial models for insurance products to support ALM goals. Integrate models with existing toolsets, manage multiple projects, review and guide others, conduct advanced research, innovate solutions, and collaborate with ALM, Risk, Pricing, Valuation, and Reporting stakeholders.

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.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Lead development, synchronization, and staffing of Offensive Cyberspace Operations (OCO) plans supporting USCYBERCOM. Drive targeting, integrate OCO/DCO, draft OPLANS/OPORDs/FRAGOs, support Joint planning teams, coordinate intelligence and capabilities, and provide situational awareness and analytic products. Communicate complex technical concepts and work across DoD/IC partners to support cyber warfare operations.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Develop and implement AI and ML systems for Defense and Intelligence sectors, focusing on deployment, scalability, and integration of technologies.

Analyze requirements and high-level architectures to develop system requirements and design specifications; lead system design, integration, and test planning; provide technical leadership, feasibility studies, and implementation guidance; develop system architecture and technical documentation; interact with government stakeholders and ensure technical integrity of deliverables.

The AI Learning Specialist designs and delivers training programs on machine learning and AI for diverse learners, focusing on accessibility and engagement.

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.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

Design, develop, and deploy AI/ML solutions to enhance enterprise Identity, Credential, and Access Management (ICAM). Build predictive analytics, anomaly detection, behavioral risk scoring, LLM/RAG solutions, and NLP capabilities. Integrate AI with ICAM platforms, create prototypes, ensure DoW security and responsible AI governance, support data preparation and model lifecycle, and collaborate with stakeholders and cybersecurity teams.

Lead and build a team that drafts and submits state-specific rating rules, underwriting guidelines, and forms; manage filing execution via SERFF, handle objection responses, coordinate cross-functional stakeholders, establish operating rhythms, improve documentation and controls, and coach staff to scale filing expertise and regulatory execution.

Lead partner analytics by building scalable dashboards, forecasting and ROI models, and experimental designs to evaluate and optimize partner performance. Collaborate with Product, BD, Marketing, and Data Engineering to improve pipelines, translate insights into recommendations, and mentor analysts to scale partner growth.

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Lead and build Adelphi's end-to-end talent acquisition function, focusing initially on sourcing and closing Forward Deployed AI Engineers for defense and intelligence customers. Own full-cycle recruiting, structured scorecard-based interviewing, candidate experience, offer negotiation, recruiting infrastructure, and vendor/tool management while partnering with executive leadership.

Early-to-mid career Product Manager to translate customer needs into product requirements for AI-driven scientific SaaS. Work cross-functionally with engineering, AI/ML, solutions, and commercial teams to prioritize backlog, run discovery, support launches, gather customer feedback, and define product metrics for customers in chemistry, manufacturing, and energy.

Embed with customers to discover needs, design and implement production-grade integrations (APIs, data pipelines, event-driven workflows), deploy and troubleshoot solutions, train IT teams, and drive adoption and measurable business outcomes.

Own strategy and execution to expand Pagaya's auto asset class: drive end-to-end enterprise sales to OEMs, banks, and dealers; close complex deals; build C-level relationships; coordinate across Risk, Tech, Product, Legal, and Data; monitor market trends; and evangelize Pagaya externally.

Lead strategy and execution to prevent, detect, and disrupt digital fraud, scams, and money mule activity. Build and mentor data science and analytics teams, develop ML predictive models, partner with product, cyber, vendors, and risk teams, and drive prevention-first controls, compliance, and operational efficiencies across payments, onboarding, and authentication channels.