Top Machine Learning Jobs in Baltimore, MD

Build full-stack marketplace features for search, ranking, and renter-facing products while supporting ML Ops infrastructure. Develop model deployment pipelines, feature stores, real-time data pipelines, monitoring, and production integrations with data scientists. Write tested code, review contributions, plan technical work, and improve system performance, reliability, and scalability. Collaborate with product and globally distributed engineering teams.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Design, implement, and scale web and data pipeline solutions focused on generative AI and LLMs. Improve AI/ML infrastructure for model development, training, deployment, and prompt engineering. Partner with product and stakeholders to prioritize applied AI use cases, ensure PHI security and regulatory compliance, mentor junior engineers, and contribute APIs and integrations to embed generative AI into healthcare applications.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

Design, build, test, deploy, and maintain production AI capabilities (agents, workflows, models, MCP servers, APIs) across a SaaS dental platform. Train and operate ML models, implement RAG and vector DB solutions, use AI-assisted tools, collaborate in design and code reviews, and support production reliability and monitoring.

Develop AI-powered solutions for personalized content and marketing. Collaborate with teams to optimize ML models and pipelines at scale, mentoring engineers and driving strategic growth initiatives.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

The role involves developing, training, and deploying machine learning models, building data pipelines, and collaborating with teams for integration and performance optimization. It supports the ML lifecycle from data collection to monitoring.

Support customer network operations with systems engineering and project management by analyzing AI/ML proposals, coordinating activities, and improving processes.

Lead AI/ML strategy, architecture, development, deployment, and operationalization for CMS mission environments. Design predictive analytics, automation, and decision-support systems; oversee data pipelines, MLOps, model validation, explainability, and bias mitigation. Integrate secure AI solutions with cybersecurity, governance, compliance, and risk management requirements. Develop RAG and agentic AI capabilities, transition prototypes to production, advise stakeholders, support audits and governance reviews, and mentor engineering teams.

Design and implement LLM-powered and agentic AI workflows integrated with mission platforms. Capture user workflows, translate decision points into executable logic, streamline analytics, ensure secure production operation, and collaborate with operators, analysts, and leadership in classified environments.

Designs, builds, deploys, and operates production AI/ML and LLM systems. Responsibilities include developing RAG pipelines, fine-tuning and evaluating models, managing end-to-end MLOps lifecycles, establishing monitoring and governance practices, mentoring engineers, and leading complex cross-functional client and internal initiatives.

The Machine Learning Engineer will design, build, evaluate, and deploy ML systems, collaborating on projects in safety, security, and intelligence applications, while owning projects from initial research through to production.

Define and execute predictive modeling and ML strategy for credit risk, servicing, and collections. Lead cross-functional initiatives, oversee model development, deployment, and monitoring, advise senior leadership on data strategy, represent analytics in regulatory and audit forums, and mentor peers while fostering organizational partnerships.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Build and deploy deep-learning models for financial market data, owning the full workflow from raw data and experiments through daily production inference. Responsibilities include managing small-scale GPU infrastructure, conducting leakage-proof and regime-aware evaluations, reproducing research, reading recent AI literature, and communicating findings. The role requires strong ML fundamentals, Python and PyTorch expertise, hands-on infrastructure skills, rigorous experimentation, and self-directed remote work. Trading experience is not required.

Leads the technical direction of an autonomous vehicle perception stack. Responsibilities include architecting scalable perception systems, developing scene detection and runtime activation, improving robustness against sensor degradation and adverse weather, integrating multimodal models, managing ML and data pipelines, monitoring field performance, leading testing and feature delivery, and mentoring engineers. Requires strong robotics, computer vision, machine learning, real-time systems, software architecture, and team leadership experience.

Lead ML technical strategy for Live Ads, designing and deploying forecasting, targeting, bidding, auction, and yield-optimization models. Build and evaluate large-scale production ML systems, create online/offline evaluation frameworks, partner with product to set objectives and trade-offs, and communicate results to stakeholders and leadership.

Lead and grow a senior machine learning and causal inference research team; define long-term research strategy; balance foundational and applied projects; partner cross-functionally to translate research into product impact; build external academic collaborations, publications, and a high-performing research program.

Lead ML initiatives to improve Sales productivity and automation: define roadmap, design and deploy production ML models and AI agents, build measurement pipelines with analytics engineers, iterate from performance and seller feedback, and act as senior technical partner across product, engineering, and Sales to drive measurable revenue impact.

Lead design and execution of a modernized, Python-native AI/ML platform for AIMS: define architecture, drive migration, build migration tooling, ensure scalability, observability, cost optimization, and reliability, prototype GenAI operational tooling, and set org-wide technical standards.