Top Machine Learning Jobs in Baltimore, MD

Founding CTO will architect and build Cure’s AI-native customer engagement and recovery platform. Responsibilities include developing conversational agents, decisioning and personalization systems, evaluation and feedback loops, enterprise integrations, and secure, explainable infrastructure. The CTO will work with customers, lead product and engineering execution, recruit early technical talent, and establish the organization needed to scale a reliable platform for regulated industries.

Research and prototype transformer-based and graph-based foundation models using customer, transaction, and merchant data. The intern will design and train models, run rigorous experiments, evaluate results, and develop working prototypes. Responsibilities include collaborating with Product, Engineering, Analytics, and Applied Science teams to improve customer experience, operational efficiency, and business outcomes.

Write and adapt technical and non-technical content across social media, blogs, founder communications, and company announcements. Translate research papers and technical findings into accessible writing, maintain distinct brand and founder voices, verify claims, collaborate with stakeholders, repurpose content across channels, and independently manage deadlines for a part-time contract.

Design and implement automated testing frameworks for data platforms, pipelines, distributed processing systems, and machine learning workflows. Develop strategies for data quality, schema integrity, drift, model accuracy, bias, regression, performance, and resilience. Investigate production failures, conduct exploratory testing, embed continuous validation into delivery processes, collaborate across engineering and data teams, and mentor junior SDETs.

Own the strategy, roadmap, and execution of Dave’s promotions and engagement products. Lead ML-driven personalization, experimentation, promotion targeting, app engagement surfaces, and retention initiatives. Partner closely with Engineering, Data Science, Marketing, Operations, Growth, and senior leadership to deliver measurable outcomes. The role requires quantitative rigor, product judgment, technical fluency, and experience building engagement, retention, or loyalty programs in high-volume B2C technology environments.

Conduct computational discovery and optimization of rare-earth-lean and rare-earth-free permanent magnets. Execute DFT and multiscale simulations, develop machine-learning workflows and interatomic potentials, generate high-throughput materials data, and connect atomistic predictions to microstructure and manufacturing processes. Collaborate with experimental teams to validate models, contribute to publications, patents, project planning, and technical presentations, and support junior researchers.

Leads the company’s global Revenue Operations organization, including Field Operations, Marketing Operations, Data Analytics, forecasting, GTM planning, territory and quota design, revenue systems governance, and AI-enabled automation. Partners with the CFO, CRO, CMO, and Board to improve forecast accuracy, pipeline health, sales productivity, retention, data reliability, and Salesforce and RevTech execution. Builds and develops a high-performing organization supporting growth from approximately $550 million to more than $1 billion in ARR.

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build backend services, APIs, data-processing systems, and frontend features for Northbeam’s marketing intelligence platform. Work on experimentation, near-real-time data infrastructure, AI-powered workflows, data integrations, RBAC, and end-to-end product delivery. Collaborate with data scientists, product managers, designers, and senior engineers while contributing across backend, frontend, pipelines, and infrastructure in a fast-moving environment.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages agile sprint planning, release delivery, DevOps metrics, and cross-functional project plans. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, manages team members, and ensures reliable, well-governed data solutions for business intelligence and reporting.

Leads end-to-end data architecture, engineering pipelines, ETL, data modeling, quality monitoring, and Tableau BI platform operations. Partners with stakeholders to define requirements, manage project plans, lead agile sprints, and deliver reliable data solutions. Establishes governance, security, performance, and continuous improvement practices while developing AI-enabled analytics capabilities. Mentors data engineers and manages team performance, documentation, dependencies, and delivery across multiple workstreams.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Support national security clients by assessing AI and ML capabilities, analyzing mission environments, designing AI adoption roadmaps, developing operational frameworks, and creating mission-focused demonstrations. The role evaluates AI risks and vulnerabilities, supports exercises and wargaming, collaborates with technology and research partners, presents solutions to leadership, and provides data-driven recommendations for implementing mission AI capabilities.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Develop machine learning and advanced analytics models for sparse, noisy, and adversarial intelligence datasets. Translate analytic questions into technical requirements, assess data quality, recommend technical approaches, communicate findings to technical and non-technical stakeholders, and support automated analytics workflows for national security missions.

Leads software engineering teams developing web applications for data management, characterization, ingestion, and compliant data movement. Extracts value from structured and unstructured data using statistical methods and machine learning, develops queries and visualizations, defines software processes, ensures quality, and collaborates with mission customers. The role also requires technical leadership, requirements analysis, database development, and experience with big data, analytic frameworks, metadata, and intelligence-related network data.

Develops software and data solutions for intelligence-community data management. Responsibilities include extracting value from structured and unstructured data, applying statistical and machine-learning methods, developing data characterization and ingestion techniques, creating database queries, supporting web applications, analyzing requirements, and leading software development teams. The role requires software engineering experience, cloud and data-processing knowledge, and a TS/SCI clearance with polygraph.

Develop and sustain cloud software and distributed systems supporting cyber intelligence and AI/ML capabilities. Responsibilities include requirements analysis, Java software development, cloud deployment, integration, testing, RESTful services, analytics, Hadoop ecosystems, Linux systems, and technical documentation. The role requires AWS expertise, Kubernetes, infrastructure as code, CI/CD, microservices, large-scale data processing, and active TS/SCI clearance with polygraph.

Designs, develops, tests, deploys, monitors, and improves machine learning algorithms and production models for business and national security missions. Integrates data pipelines, evaluates model performance, ensures data and output quality, and transforms prototypes into scalable solutions. Requires expertise in machine learning frameworks, statistical analysis, model architecture, software and data engineering principles, and verification and validation. May lead distributed teams and serve as a technical point of contact.

Develop and sustain cloud software and AI/ML capabilities for national security applications. Responsibilities include requirements analysis, Java-based distributed software development, AWS cloud integration, RESTful services, testing, deployment, troubleshooting, and technical documentation. The role works with Hadoop and big-data technologies, Linux, Kubernetes, infrastructure as code, CI/CD, LLMs, RAG, and agentic AI. An active TS/SCI clearance with polygraph and at least five years of software engineering experience are required.

Designs, develops, tests, deploys, and improves machine learning models and algorithms for business and national security mission requirements. Responsibilities include selecting datasets, performing statistical analysis, integrating data pipelines, training and monitoring models, interpreting performance metrics, conducting testing and evaluation, and supporting scalable AI/ML solutions. The role may coordinate distributed teams and requires strong communication, analytical, and problem-solving skills.