Top Machine Learning Jobs in Baltimore, MD

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The Enterprise Account Executive drives revenue through high-value contracts, managing complex sales processes and relationships with executive stakeholders in financial services.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Designs AI/ML pilots, predictive analytics and anomaly detection models, behavioral analytics, content classification, personalization algorithms, and end-to-end machine learning pipelines. Develops prototypes and production-ready solutions, evaluates AI technologies, supports experimentation, integrates ML insights into dashboards, and ensures model governance, explainability, bias mitigation, accuracy, and security compliance in federal and DoD-aligned environments.

The Data Scientist will apply machine learning and statistical analysis to support mission analysis, develop analytical tools, and visualize complex datasets for insights.

The role involves analyzing and developing algorithms for scientific image correction, object detection, and preprocessing techniques, especially for microscopy data, optimizing performance for large datasets and deploying machine learning models.

The role involves developing innovative IT solutions in healthcare, engaging with federal clients, and providing strategic guidance. Leadership in technology and solution architecture is essential, alongside collaboration with interdisciplinary teams.

Design and apply statistical, mathematical, and computational methods to extract value from large, messy datasets. Build, evaluate, and communicate analytic and ML models, translate mission needs into technical requirements, and recommend solutions while explaining results to non-technical stakeholders.

Lead go-to-market and partner development for Personal Loans and Point-of-Sale products. Own end-to-end enterprise sales, build C-level relationships, close complex deals, and coordinate cross-functional teams to launch AI-driven consumer lending integrations and scale market presence.

Author and verify rigorous multiple-choice computer science assessment items across many CS domains to create gold-standard benchmarks for AI research. Produce correct answers, nine plausible distractors, solution explanations, difficulty labels, and authoritative references. Review and edit questions for technical accuracy, clarity, and reproducibility. Contribute asynchronously as an independent contractor, ~10+ hours/week, fully remote.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

Define and execute the product roadmap for AI-driven on-chain monitoring tools to detect blockchain threats. Collaborate with engineering, research, and stakeholders, analyze market and customer feedback, prioritize features, ensure timely delivery, and maintain transparent communication across teams.

Lead a global manufacturing engineering organization to standardize processes, drive lean continuous improvement, deploy automation and Industry 4.0 solutions (cobots, ML), and ensure safety, traceability, and zero-defect customer quality across international sites.

Design, implement, train, evaluate, optimize, and deploy advanced AI/ML solutions including Agentic AI, RAG, and LLMs for constrained/edge environments; translate mission stakeholder requirements into scalable, resource-aware models and recommend performance improvements.

Provide advanced discovery support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, automate workflows, and collaborate with analysts and mission stakeholders to identify patterns and drive investigations.

Provide advanced discovery and analytical support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, document methods and collaborate with analysts and engineers to support mission-focused discovery efforts and adopt new analytical technologies.

Perform advanced data science and machine learning on large NSA/CSS datasets. Design, implement, and validate analytic models, manage and curate data, extract actionable insights, and communicate technical conclusions to non-technical stakeholders. Translate mission needs into technical requirements while staying aware of collection and processing constraints.

Develop and apply statistical and machine-learning methods to large, messy NSA/CSS datasets. Design, implement, and evaluate analytic models; perform data cleaning, management, visualization, and inference; translate mission needs into technical requirements and communicate findings to non-technical stakeholders.

Develop, test, and apply quantum computing workflows or quantum-inspired approaches to analyze sensing and energy-industry datasets. Work may include signal processing and machine learning. Produce technical documentation, presentations, and publishable research (journal or conference paper).

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Partner with a retained search firm to place software engineers and quant developers at top hedge funds. Provide candidate flow from your network; firm supplies clients, process, and fees. Remote, commission-only role with placement and pipeline incentives. Apply with placement examples, outreach metrics, and a candidate submission.

Lead technical strategy and hands-on engineering for Airbnb's AI-powered ads growth engine. Architect scalable systems for creative generation, bidding, budget optimization, and audience management across paid channels. Write and review code, set standards for availability and quality, collaborate with Marketing/Product/ML teams, roll out staged automation, and mentor engineers to advance platform autonomy while ensuring brand safety and human-in-the-loop controls.