Top Machine Learning Jobs in Baltimore, MD

Serve as principal AI technical advisor to translate emerging AI capabilities into workforce development and mission-focused training. Collaborate with researchers and engineers to define skills, build curriculum, create labs and use cases, support demonstrations and workshops, evaluate technologies, and guide AI governance, MLOps, LLMs, agentic AI, and cybersecurity integration for national security missions.

As a Software Developer Intern, you will develop software solutions in an agile environment while collaborating with a dynamic team, focusing on backend and frontend engineering, and applying AI and machine learning.

Conduct R&D on AI/ML and machine translation for SIGINT and cybersecurity applications. Build research prototypes, evaluate and enhance MT tools and text-processing pipelines, and implement containerized solutions. Resolve complex AI technical problems and collaborate with senior technical staff.

Design, build, evaluate, and deploy AI agents and machine learning automation for engineering, product, delivery, and customer-facing workflows. Integrate AI into existing software, collaborate with technical and healthcare stakeholders, optimize systems for accuracy, latency, cost, and safety, and document development processes. The role emphasizes LLMs, agentic frameworks, production ML systems, human-in-the-loop safeguards, and healthcare-aware solutions.

Provide technical writing support for proposals, RFIs/RFPs, white papers, SOPs, user guides, and program deliverables. Translate technical/AI/ML content for non-technical audiences, standardize templates, maintain document libraries, create diagrams, ensure quality and compliance with DoD/intelligence standards and ISO 9001, and collaborate with SMEs and cross-functional teams.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

Define and lead enterprise-wide AI/ML architecture strategy, standards, governance, reference architectures, and adoption roadmaps. Design scalable architectures for generative AI, LLM applications, RAG, AI agents, MLOps, data platforms, cloud infrastructure, security, and enterprise applications. Lead technology evaluations, architecture reviews, modernization strategies, responsible AI controls, vendor decisions, and transformation programs while advising executives and technical teams.

Full-cycle quota-carrying Account Executive selling Bold Metrics' SaaS fit and sizing solutions to apparel brands. Own outreach through contracting, build ROI cases, manage cross-functional coordination, develop outbound strategies, leverage AI to scale workflows, track funnel metrics, and feed customer insights to Product.

Build and operate scalable ML infrastructure for data preparation, model training, evaluation, deployment, monitoring, and rollback. Develop AWS and Kubernetes platforms, reproducible pipelines, dataset and model versioning, observability, testing, security controls, and self-service tooling. Partner with autonomy, software, data, simulation, and security teams to support cloud-to-edge ML workflows and reliable production deployments.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions, develop end-to-end data pipelines and applications, integrate machine learning models, and guide enterprise customers through implementation and adoption. Collaborate with engineering, product, support, project managers, and customer stakeholders while delivering projects, documenting solutions, resolving issues, and creating reusable frameworks. Travel to customers approximately 20% of the time.

Leads the strategy, roadmap, and product organization for NovaOS, an enterprise AI operating system. Oversees a portfolio spanning data, semantic, agentic, and experience layers; partners with business, engineering, and technology leaders to prioritize investments and deliver measurable outcomes. Establishes product governance, lifecycle practices, success metrics, and feedback mechanisms while building and mentoring a high-performing product management team.

Leads enterprise AI-focused sales campaigns across a regional territory, advising C-suite and technical AI buyers on data platform and infrastructure solutions. Partners with account executives, solution architects, engineers, product teams, GSIs, and channel partners to develop use cases, reference architectures, ROI frameworks, and joint go-to-market strategies. Owns pipeline discipline, forecasting, account planning, complex deal orchestration, and AI solution positioning for enterprise workloads.

Apply mathematics, statistics, and computer science to design and implement ML and advanced analytic methods for large, messy NSA/CSS datasets. Curate and model data, perform statistical and numerical analyses, translate mission needs into technical requirements, and clearly communicate findings to non-technical audiences. Maintain awareness of collection, processing, and analytic capabilities to recommend solutions.

Develop and deploy deep learning models for AI-guided antibody drug discovery and target identification. Design experiments, build systems biology models, and create in silico and in vitro validation approaches. Collaborate with biological and wet-lab teams, present findings, publish high-impact research, and mentor scientists and engineers.

Hands-on analytics role building and maintaining ML and generative AI models for retail/CPG datasets. Conduct descriptive and customer/product/transaction-level analysis, create Tableau dashboards, write SQL (BigQuery) and Python data workflows, design LLM embedding solutions, build simulations, ensure data quality, work with senior stakeholders, and mentor junior team members.

Lead development and implementation of machine learning and generative AI solutions for retail/CPG data. Perform customer, product, and transaction-level analysis, build models and simulation applications, create Tableau visualizations, write SQL (BigQuery) and Python ETL, ensure data quality, work with client stakeholders, and mentor junior analysts.

Leads the engineering strategy, architecture, development, operations, and continuous improvement of an enterprise agentic AI foundation. Establishes scalable engineering standards, operating models, governance, and reusable platform services that enable AI products and intelligent workflows. Partners with product, data, infrastructure, security, and business teams to modernize existing systems, guide cross-functional delivery, monitor operations, and communicate priorities, risks, and investment needs to senior leaders. Manages and develops product or engineering leaders while driving measurable business value.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Generate and qualify inbound and outbound leads through high-volume phone, email, and social outreach. Schedule introductory meetings and demos, maintain accurate CRM records, and collaborate with marketing on lead-nurturing strategies. The role requires strong communication, resilience, time management, and interest in building a SaaS sales career. This is a fully remote position open to candidates anywhere in the United States.

The Customer Success Engineer will lead deployments, serve as a technical advisor, and support customer engagement, focusing on product enhancements and user experience.

OneSix is seeking motivated professionals for future opportunities in data and AI consultancy. Interested candidates should submit their resumes to stay in touch.

The Senior Director leads research in Application Platforms & AI, guiding clients in AI strategy and market trends, while producing critical research content and engaging with clients directly.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

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