Top Machine Learning Jobs in Baltimore, MD

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, ML lifecycle workflows, cognitive performance metrics, and data-mining capabilities. Participate in SAFe Agile delivery, author user stories, support DevSecOps integration, resolve technical impediments, and contribute to transition and knowledge-transfer materials. The role requires collaboration with architecture, product, and government teams in a cleared defense environment.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Support defense cybersecurity efforts by mapping and curating large datasets, applying AI/ML (including generative AI) and statistical methods, building models and visualizations/dashboards, documenting metrics, and communicating analytic conclusions to technical and non-technical stakeholders.

Support the PMO with data-driven operational analysis, process optimization, and contract performance monitoring. Produce and maintain program deliverables and reports, centralize administrative workflows, coordinate program and government personnel, support onboarding and subcontract/security coordination, and apply AI/ML tools to automate PMO workflows and improve reporting accuracy.

Reviews and evaluates engineering, infrastructure, and platform documents and work products for technical accuracy, completeness, clarity, consistency, and relevance. Provides structured feedback, identifies errors and gaps, compares technical approaches, and follows defined evaluation frameworks. Requires independent work, strong communication, analytical ability, and professional engineering judgment. The role is fully remote and supports varied technical evaluation projects as an independent contractor.

Drive net-new enterprise customer acquisition from lead generation through contract closure. Build C-level relationships, develop account strategies, manage CRM pipelines and forecasts, negotiate complex contracts, and close large AI, machine learning, and data talent engagements. Partner with marketing, customer success, and talent teams to deliver customer outcomes and expand Factored AI’s enterprise footprint.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Leads technical discovery, enterprise solution architecture, technology assessments, and implementation roadmaps for consulting clients. Designs architectures across applications, data, cloud, integration, and enterprise technology; advises executives on technology decisions, scalability, security, cost, and risk. Guides technical teams, resolves complex delivery challenges, develops proofs of concept and reusable standards, evaluates emerging technologies, and supports proposals, estimates, statements of work, and new business development.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Develop and integrate large-scale data applications using Accumulo, Hadoop, Kafka, Spark, Yarn, Python, and JavaScript. Implement predictive analytics, graph algorithms, and machine learning models; design client/server systems and integration points; improve development workflows and support analytic toolchains for Intelligence Community missions.

Seeking a Senior Software Engineer with 14+ years of experience to support U.S. Government contracts in software and systems development, specifically in Cyber Security solutions and Agile Software Development.

GliaCell seeks a Principal Software Engineer to provide technical expertise on a government contract, supporting critical software and systems. Key responsibilities include working independently and with teams, leveraging extensive software engineering experience.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.

Provides clinical expertise, customer support, product demonstrations, training, onboarding, and certification for Transpara Breast AI across the USA and Canada. Supports research initiatives, clinical adoption, customer engagement, NPS tracking, workshops, webinars, educational content, case curation, and customer feedback resolution. Collaborates with Sales, Customer Success, Marketing, Engineering, Innovation, and Product Management teams. Requires medical imaging experience, preferably in mammography or breast imaging AI, strong communication and training skills, and approximately 20% travel.

Drive enterprise sales growth across North American banking, credit card, and financial services accounts. Develop strategic account and business development plans, build customer and partner relationships, close complex multi-million-dollar technology deals, and engage C-level stakeholders. Represent the company at industry events, collaborate with commercial and leadership teams, manage pipeline forecasting and reporting, and apply MEDDPICC methodology. The role focuses on cybersecurity, fraud prevention, authentication, and SaaS or cloud solutions, with 50% domestic and international travel.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.