Top Machine Learning Jobs in Baltimore, MD

Designs, develops, deploys, and maintains AWS cloud-native applications and infrastructure. Builds CI/CD pipelines, Infrastructure as Code solutions, and containerized deployments using Terraform or CloudFormation, Kubernetes, and Docker. Supports integration, testing, monitoring, troubleshooting, observability, performance optimization, scalability, security, and operational sustainment for mission-critical systems. Collaborates with software, systems, and cloud engineering teams in Agile environments.

Develop, maintain, and enhance software systems supporting AI-driven automation and efficiency. Design, develop, and deploy scalable AI/ML models and tools, including prompt engineering and retrieval-augmented generation solutions. Collaborate with stakeholders to translate mission requirements into technical solutions and analyze data structures and objects in support of a classified Intelligence Community customer.

Develop and lead software solutions for data ingestion, characterization, enrichment, and movement across intelligence-community mission systems. Extract insights from structured and unstructured data using statistical methods, machine learning, and analytic tools. Define requirements, develop queries and data flows, collaborate with customer teams, recommend technologies, lead software teams, oversee development processes and quality control, and support large-scale data processing using technologies such as Java, Python, Scala, Spark, Hadoop, and NiFi.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, maintenance, monitoring, and issue resolution. The role uses distributed NoSQL technologies, big-data frameworks, cloud services, AI/ML algorithms, LLMs, RAG, and agentic workflows to support national security missions.

Develop and maintain cloud software supporting AI/ML and national security missions. Responsibilities include requirements analysis, software development, AWS cloud integration, distributed systems engineering, microservices, testing, troubleshooting, and sustainment. The role uses Java, Linux, Kubernetes, infrastructure-as-code, CI/CD, big-data technologies, RESTful services, and AI/ML frameworks including LLMs and RAG. An active TS/SCI clearance with polygraph is required.

Develop cloud software and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java software development, RESTful services, distributed systems, cloud integration, infrastructure as code, containerization, testing, sustainment, and issue resolution. The role requires experience with Hadoop and big-data technologies, Linux, software integration, technical documentation, and secure solutions supporting national security missions.

Develop and validate predictive and prescriptive machine learning models for cyber defense decision-making. Perform statistical analysis, anomaly detection, event correlation, and operational research to identify trends and characterize threats. Translate mission requirements into analytic solutions, document methodologies, and mentor junior data scientists. The role requires advanced Python, R, and SQL skills, model deployment experience, strong statistical expertise, and a Top Secret clearance with the ability to obtain and maintain SCI eligibility.

Develop AI tools, machine learning systems, predictive analytics, data visualizations, and large-scale Multi-INT data fusion capabilities for USCYBERCOM J2 intelligence operations. Analyze and merge disparate data using R, Python, and SQL; create Power BI and Tableau visualizations; produce analytic reports; and collaborate with intelligence stakeholders to prioritize capability gaps.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, enhancement, monitoring, and issue resolution. The role uses distributed NoSQL and big-data technologies, cloud architecture, AI/ML tools, microservices, containers, and infrastructure-as-code. Candidates must have at least two years of relevant software engineering experience and an active TS/SCI clearance with polygraph.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, enhancement, maintenance, and issue resolution. The role involves distributed NoSQL systems, big-data technologies, cloud scalability, AI/ML algorithms, LLMs, RAG, agentic workflows, and software testing within a mission-driven national security environment.

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.

Leads the design and delivery of AI/ML, advanced analytics, predictive modeling, data fusion, simulation, optimization, and decision-support capabilities for aviation modernization. Transforms complex program, engineering, operational, and aviation data into executive-ready insights; guides agentic AI and analytical workflows; establishes model evaluation, governance, reproducibility, and responsible AI practices; mentors technical contributors; and briefs FAA, government, and senior organizational stakeholders.

Designs, develops, tests, deploys, and improves machine learning models for business and mission requirements. Responsibilities include selecting datasets, performing statistical analysis, integrating data pipelines, training and retraining models, monitoring performance, applying evaluation protocols, and overseeing system integration. The role requires knowledge of model architecture, infrastructure, data engineering, governance, and scalable AI/ML solutions, with potential leadership of distributed teams and customer engagement.

Leads structured analysis of technical, operational, and programmatic information using AI-enabled tools and quantitative reasoning. Validates AI-generated outputs, assesses evidence, and translates findings into executive briefings, dashboards, reports, narratives, and talking points. Designs visualization strategies for varied stakeholder audiences and collaborates with leadership, data teams, AI/ML teams, and subject matter experts. Maintains editorial rigor, analytical clarity, and strategic judgment in high-visibility government and technical environments.

Develops machine learning, statistical, data mining, graph-based, and large language models for national security applications. Analyzes datasets, creates feature vectors, prototypes and validates algorithms, builds experiments, generates visualizations, and implements analytics in production frameworks. The role collaborates with subject matter experts, optimizes GPU-based model training, supports LLM and RAG solutions, and guides analytic development efforts. An active TS/SCI clearance with polygraph is required.

Develop and operationalize machine learning, statistical, graph-based, and AI analytics for national security missions. Responsibilities include applied research, experimentation, data visualization, feature engineering, model development and validation, LLM and NLP optimization, GPU-accelerated computing, automation, production integration, and customer collaboration. The role also guides analytic efforts and translates complex data into actionable insights.

Owns US customer and commercial operations for a healthcare hardware and AI company. Responsibilities include forecasting camera demand, managing manufacturing partners and inventory, launching repair and replacement services, executing and negotiating customer contracts, managing legacy obligations, creating SOPs and policies, and automating operational workflows with AI tooling. The role is remote, US-based, with occasional travel for customer engagements and team offsites.

Own adoption, retention, and expansion across a pooled portfolio of scaled customers through digital-first programs. Provide technical API integration guidance, troubleshoot issues, run demos, and create reusable enablement assets. Design automated customer journeys, webinars, self-service resources, and workflows that accelerate activation. Monitor usage and health data, identify expansion opportunities, report KPIs, and build scalable systems that reduce recurring manual work.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

Lead corporate communications strategy and execution: own product and model launches, media and executive communications, narratives, social presence, and major company moments. Translate technical research and product advances into compelling stories for developers, enterprise buyers, partners, and analysts. Partner cross-functionally with CEO, CMO, product, brand, and demand gen to build credibility and influence in the voice AI ecosystem.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

Develop automated security evaluation and authorization capabilities using Python, statistical analysis, machine learning, anomaly detection, and ETL pipelines. Build reliable dataflows integrating network, security, and compliance data to support continuous monitoring, RMF automation, reporting, and security decision-making. The role requires extensive analytics and programming experience and a TS/SCI clearance with polygraph.

Lead capability development across full project lifecycles, guiding agile teams in reverse engineering, vulnerability analysis, exploit development, and systems security research. Develop Linux exploitation capabilities, including kernel exploitation, privilege escalation, and persistence. Apply operating-system internals knowledge, AI and machine learning, product security, cryptographic resilience, and anti-forensic techniques while mentoring cyber personnel and supporting national security missions.

Develop and deploy enterprise-scale AI and machine learning solutions for secure government and intelligence missions. Build LLM-powered workflows, agent-based automation, data pipelines, and production ML systems. Integrate AI into operational platforms, conduct model evaluation and testing, optimize workflows, and ensure scalability, reliability, security, and governance. Collaborate with mission users, analysts, software developers, and leadership while providing technical guidance and mentoring.