Top Remote Machine Learning Jobs in Baltimore, MD

Leads the strategy, roadmap, and execution of search, ranking, matching, and recommendation products for a two-sided marketplace. Oversees algorithmic performance, resource allocation, experimentation, metrics, executive communication, and cross-functional alignment across consumer and enterprise experiences. Coaches product managers and establishes scalable operating rituals while connecting technical improvements to engagement, retention, customer value, and marketplace growth.

Own customer-facing AI and data engagements from discovery through production launch. Define business value, MVPs, success criteria, and product requirements; prioritize engineering backlogs; lead technical discovery, architecture reviews, agile delivery, testing, production readiness, launch, adoption, and strategic handoff. Build C-suite relationships, translate business needs into technical requirements, resolve delivery risks, and identify future high-value opportunities.

Lead Cure from company formation through product-market validation and seed financing. Define the market wedge, secure enterprise design partners, shape an AI-native recovery product, establish data and evaluation loops, recruit the founding team, build the company narrative, and lead fundraising. The role requires enterprise AI judgment, commercial credibility, consumer-finance and regulatory understanding, and founder-level ability to create a new category.

Founding CTO will architect and build Cure’s AI-native customer engagement and recovery platform. Responsibilities include developing conversational agents, decisioning and personalization systems, evaluation and feedback loops, enterprise integrations, and secure, explainable infrastructure. The CTO will work with customers, lead product and engineering execution, recruit early technical talent, and establish the organization needed to scale a reliable platform for regulated industries.

Research and prototype transformer-based and graph-based foundation models using customer, transaction, and merchant data. The intern will design and train models, run rigorous experiments, evaluate results, and develop working prototypes. Responsibilities include collaborating with Product, Engineering, Analytics, and Applied Science teams to improve customer experience, operational efficiency, and business outcomes.

Write and adapt technical and non-technical content across social media, blogs, founder communications, and company announcements. Translate research papers and technical findings into accessible writing, maintain distinct brand and founder voices, verify claims, collaborate with stakeholders, repurpose content across channels, and independently manage deadlines for a part-time contract.

Design and implement automated testing frameworks for data platforms, pipelines, distributed processing systems, and machine learning workflows. Develop strategies for data quality, schema integrity, drift, model accuracy, bias, regression, performance, and resilience. Investigate production failures, conduct exploratory testing, embed continuous validation into delivery processes, collaborate across engineering and data teams, and mentor junior SDETs.

Own the strategy, roadmap, and execution of Dave’s promotions and engagement products. Lead ML-driven personalization, experimentation, promotion targeting, app engagement surfaces, and retention initiatives. Partner closely with Engineering, Data Science, Marketing, Operations, Growth, and senior leadership to deliver measurable outcomes. The role requires quantitative rigor, product judgment, technical fluency, and experience building engagement, retention, or loyalty programs in high-volume B2C technology environments.

Conduct computational discovery and optimization of rare-earth-lean and rare-earth-free permanent magnets. Execute DFT and multiscale simulations, develop machine-learning workflows and interatomic potentials, generate high-throughput materials data, and connect atomistic predictions to microstructure and manufacturing processes. Collaborate with experimental teams to validate models, contribute to publications, patents, project planning, and technical presentations, and support junior researchers.

Leads the company’s global Revenue Operations organization, including Field Operations, Marketing Operations, Data Analytics, forecasting, GTM planning, territory and quota design, revenue systems governance, and AI-enabled automation. Partners with the CFO, CRO, CMO, and Board to improve forecast accuracy, pipeline health, sales productivity, retention, data reliability, and Salesforce and RevTech execution. Builds and develops a high-performing organization supporting growth from approximately $550 million to more than $1 billion in ARR.

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build backend services, APIs, data-processing systems, and frontend features for Northbeam’s marketing intelligence platform. Work on experimentation, near-real-time data infrastructure, AI-powered workflows, data integrations, RBAC, and end-to-end product delivery. Collaborate with data scientists, product managers, designers, and senior engineers while contributing across backend, frontend, pipelines, and infrastructure in a fast-moving environment.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages agile sprint planning, release delivery, DevOps metrics, and cross-functional project plans. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, manages team members, and ensures reliable, well-governed data solutions for business intelligence and reporting.

Leads end-to-end data architecture, engineering pipelines, ETL, data modeling, quality monitoring, and Tableau BI platform operations. Partners with stakeholders to define requirements, manage project plans, lead agile sprints, and deliver reliable data solutions. Establishes governance, security, performance, and continuous improvement practices while developing AI-enabled analytics capabilities. Mentors data engineers and manages team performance, documentation, dependencies, and delivery across multiple workstreams.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Owns US customer and commercial operations for a healthcare hardware and AI company. Responsibilities include forecasting camera demand, managing manufacturing partners and inventory, launching repair and replacement services, executing and negotiating customer contracts, managing legacy obligations, creating SOPs and policies, and automating operational workflows with AI tooling. The role is remote, US-based, with occasional travel for customer engagements and team offsites.

Own adoption, retention, and expansion across a pooled portfolio of scaled customers through digital-first programs. Provide technical API integration guidance, troubleshoot issues, run demos, and create reusable enablement assets. Design automated customer journeys, webinars, self-service resources, and workflows that accelerate activation. Monitor usage and health data, identify expansion opportunities, report KPIs, and build scalable systems that reduce recurring manual work.

Leads engineering managers and software teams building customer-facing front-end applications and internal and external APIs. Sets technical direction, oversees delivery, manages risks and dependencies, improves engineering quality and security, develops talent, supports hiring, and partners with product and design. The role drives innovation and scalable software solutions for connected vehicle and fleet operations.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.