Top Machine Learning Jobs in Baltimore, MD

Develop and evaluate machine learning and statistical models supporting intelligence analysis. Curate, transform, and integrate data; build classifiers and anomaly detection systems; deploy and refine models in production; monitor model performance and data accuracy; collaborate with analysts and software developers; document processes and datasets; and communicate findings to stakeholders.

Leads the design, development, prototyping, and operational deployment of AI/ML applications supporting intelligence missions. Collaborates with mission stakeholders to automate complex workflows, generate insights, and deliver production-ready capabilities. Applies classification, clustering, search, retrieval, deep learning, recurrent networks, transformers, and large language models across software engineering and data science domains.

Support Computer Network Operations by applying AI and machine learning to network exploitation tools, data, and workflows. Evaluate large language models, analyze mission data using statistical methods, develop Python-based analytic and ML workflows, normalize and visualize data, and collaborate with operators, developers, and engineers to deliver mission-focused technical solutions.

Develops, integrates, tests, deploys, and maintains scalable analytics and data science tools. Works with data scientists and mission analysts to solve strategic problems, operationalize data science, and integrate solutions into customer architectures. Responsibilities include software engineering, big data development, data analysis, machine learning applications, and collaboration using Agile tools and practices.

Design, develop, test, and productize AI/ML algorithms. Responsibilities include selecting and analyzing data, training and improving models, evaluating system performance, integrating scalable solutions, validating data and model quality, and identifying distribution issues. The role requires software engineering expertise, machine learning deployment experience, and oversight of iterative verification and validation processes.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Develop and deploy AI-driven software, scalable machine learning models, RAG solutions, and fine-tuned LLMs for Intelligence Community mission systems. Responsibilities include prompt engineering, data structure analysis, AI automation, and Linux-based scripting with Python or Bash. The role is fully onsite at Fort Meade and requires active TS/SCI clearance with a full-scope polygraph.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Leads a team developing and deploying AI and machine learning capabilities for cybersecurity operations. Responsibilities include identifying use cases, assessing data readiness, developing and validating models, integrating solutions into secure DevSecOps and MLOps workflows, monitoring performance, managing AI risks, and briefing government stakeholders. The role also requires technical mentorship, collaboration with cyber experts, and compliance with federal data governance, cybersecurity, and authorization requirements.

Supports Global Technology projects involving data analysis, software development, testing, deployment, process automation, visualization, and AI adoption. The intern collaborates with technology and data teams, documents technical processes, develops user guides, participates in cross-functional projects, and explores emerging AI and data-management practices.

Own technical delivery and account health for insurer software deployments. Responsibilities include discovery, solution design, integrations, API and payload review, QA testing, defect documentation, workflow mapping, SQL-based production monitoring, incident management, and technical communication with carrier accounts and engineering teams.

Owns customer retention and expansion across a portfolio of accounts. The Renewals Manager engages customers before renewal, drives value-based conversations, analyzes usage and health data, identifies churn and expansion opportunities, and coordinates renewal execution across Sales, Finance, and Legal. The role also develops scalable engagement frameworks, presents pipeline insights to leadership, and partners with Account Executives and technical customer success teams in a B2B technology environment.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Build and scale accounting software synchronization solutions connecting BILL’s payment network with third-party accounting platforms. Lead end-to-end software design and delivery, develop scalable AWS microservices, improve platform performance, guide testing and CI/CD practices, resolve customer problems, mentor engineers, and use generative AI to accelerate development.

Develop and maintain internal applications and AI agents using Microsoft Power Platform, APIs, databases, Copilot Studio, and Azure. Support Agile delivery, automated testing, enterprise integrations, conversational AI, intelligent automation, security and compliance standards, performance monitoring, documentation, and cross-functional solution design. The role is remote with occasional travel and offers foundational development experience.

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Leads enterprise healthcare interoperability strategy, operations, connectivity, and strategic partnerships. Establishes operating models, roadmaps, KPIs, governance, and partner ecosystems across EHRs, HIEs, QHINs, payers, providers, and data networks. Translates regulatory and technical requirements into scalable capabilities, supports product and commercial growth, oversees investments and agreements, and builds high-performing teams. Requires executive leadership in healthcare data exchange, interoperability standards, regulatory strategy, operations, partnerships, and technology commercialization.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Own the AI product roadmap, prioritization, accuracy metrics, model and vendor requirements, release planning, and correction feedback loop. Partner with engineering, product, tax, QA, UX, and client success to improve document extraction coverage, accuracy, cost, latency, and filing readiness across K1x products.

Leads the full lifecycle of clinical NGS diagnostic algorithms for oncology and liquid biopsy applications. Responsibilities include algorithm design, analytical validation, design control, regulatory readiness, variant calling, QC metric development, probabilistic modeling, machine learning, and validation pipeline automation. The role serves as technical authority for algorithm changes, supports regulatory submissions, and develops solutions for low-input, fragmented, and ultra-low-frequency genomic variants.

Build and deploy agentic AI, machine learning, and generative AI solutions across distributed and cloud environments. Develop LLM features including RAG, summarization, embeddings, and prompt-engineered workflows. Evaluate model quality, hallucinations, grounding, and production performance. Use Python and SQL to develop models, collaborate with technical and business teams, and manage end-to-end AI/ML projects from data preparation through deployment and monitoring.

Serves as a permitting and plan review domain expert for engineering, product, design, data, and go-to-market teams. Reviews permit applications, plan sets, submittals, and AI outputs; interprets building codes; identifies missing or noncompliant information; defines approval and revision criteria; labels examples for ML systems; synthesizes jurisdictional requirements; and develops rubrics, checklists, documentation, and benchmarks. Requires approximately 5+ years of hands-on permitting or plan review experience and fluency in applicable building codes.

Leads the Product Acquisition engineering team and drives technology strategy, architecture, software delivery, DevOps maturity, platform adoption, vendor management, and operational excellence. Partners with product managers on roadmaps and business outcomes, ensures engineering standards and production support, and coaches, recruits, and develops engineers and consultants. Requires strong software development, product operating model, project management, stakeholder communication, and technical leadership experience.