Top Machine Learning Jobs in Baltimore, MD

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

Lead discovery, definition, and delivery of B2B SaaS product features. Own customer research, data-driven prioritization, UX collaboration, KPI tracking, and cross-functional execution with engineering, marketing, and customer success.

Lead enterprise design and deployment of a global operating model for bovine IVF production. Drive standardization, SPC, automation, AI/analytics, Lean/Six Sigma, quality systems, and continuous improvement to scale consistent, high-throughput production across labs and field operations.

Lead pre-sales for Workday Financials-focused AI SaaS: run discovery, demos, technical workshops, respond to RFIs/RFPs, collaborate with Product/Engineering, support sales expansion and market initiatives.

Own a portfolio of large manufacturing accounts, drive net-new pipeline and full-cycle enterprise deals, build C-suite relationships, translate AI/vision capabilities into ROI, execute land-and-expand strategies, and travel 40-50% for on-site engagement and executive reviews.

Lead the commercial engine: build partnership function, close enterprise platform partnerships, own full marketing (positioning, content, paid, website), design AI-leveraged GTM stack, translate product-market fit, run pipeline and commercial reporting, and hire a team when metrics justify growth.

Lead development and integration of predictive pricing models for home insurance, modernize state rating plans, support rate filings, monitor performance and recommend pricing actions, analyze competitor strategies, develop pricing for expansion, build internal pricing analytics software, and coach/mentor analysts.

Lead and coordinate end-to-end fraud risk engagements, structure analytical solutions, guide analytics teams, communicate with clients and senior stakeholders, manage timelines, build fraud rules/governance, and identify additional client opportunities.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Design and implement conversational AI solutions using NLP, ML, and LLMs. Build ETL pipelines and integrate relational and NoSQL databases. Develop Voice AI interactions on Vertex AI, work with Nuance IVR and Microsoft platforms, and write production-grade Python code.

Design, build, and deploy conversational AI solutions on the Kore.AI platform. Create and configure chatbots (intents, entities, dialog flows), integrate with back-end systems and REST APIs, and develop responsive user interfaces using ReactJS alongside UI/UX designers.

Design, build, and deploy conversational agents using Microsoft Copilot Studio. Gather stakeholder requirements, implement NLU/NLP, integrate with enterprise systems, test and refine agents, and provide documentation and user support for HR, policy retrieval, and procure-to-pay automations.

Lead digital transformation engagements by developing strategy, advising executives, and delivering end-to-end projects. Drive adoption of cloud, AI, data analytics, and emerging technologies while managing multidisciplinary teams, ensuring compliance, and measuring outcomes through KPIs.

Lead cross-functional technical programs from planning through delivery. Coordinate engineering, architecture, and business teams to launch capabilities, manage risks, prioritize intake, define KPIs and processes, influence technical strategy, and ensure timely delivery of large customer-facing initiatives.

Lead cross-functional technical programs, coordinate launches, develop scalable processes and KPIs, manage risks and intake prioritization, influence product and technical strategy, and drive timely delivery with engineering and business stakeholders.

Lead design, implementation, and scaling of enterprise Palantir data solutions. Oversee cross-functional teams to build data pipelines, integrations, governance, and advanced analytics (ML/AI). Manage project delivery, stakeholder engagement, performance optimization, and mentor senior engineers and analysts while ensuring security and regulatory compliance.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Gather and document functional requirements for agent-facing servicing applications, define integrations/APIs with core banking systems, consult with stakeholders, identify AI/ML opportunities (fraud, KYC, automation), support testing and implementation, and act as SME while managing risks and compliance.

Lead procurement strategy and supplier selection for inbound products and services. Manage RFIs/RFPs, negotiate contracts with Legal, administer Coupa purchase orders, reconcile invoices, and apply AI/ML and reporting tools to optimize procurement processes while collaborating with cross-functional senior stakeholders.

Lead technical execution across backend, frontend, mobile, AI, and data. Translate ambiguous product goals into technical plans, unblock hard engineering problems, coordinate cross-domain work, raise engineering quality, define hiring needs and technical evaluation standards, and build internal AI workflows to foster an AI-native engineering culture.

Perform data and computer science analysis to support customer operations using advanced machine learning, statistical methods, and programming (Python, Java, C/C++). Curate and process large, messy national agency datasets, build models and inferences, translate mission requirements into technical solutions, and communicate results across teams. TS/SCI with polygraph clearance required.

The Data Scientist Level 4 develops strategies to extract insights from large datasets, utilizing advanced analytics and machine learning techniques. Responsibilities include statistical analysis, modeling, and effectively communicating findings to non-technical audiences.

Lead Offensive Cyberspace Operational planning supporting USCYBERCOM, integrating cyber operations, and collaborating on strategic objectives through various cyber plans and intelligence activities.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.