Top Remote Machine Learning Jobs in Columbus, OH

Develop and productionize machine learning and AI systems across document intelligence, language, vision, and multimodal applications. Design datasets, models, training pipelines, and evaluation frameworks; fine-tune foundation models; run GPU experiments; and build reusable AI capabilities. Partner with engineering and product teams on deployment, monitoring, reliability, and improvement while communicating technical tradeoffs and delivering measurable business outcomes in regulated financial services.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Leads public health informatics and enterprise data engineering for a cloud-native Databricks Lakehouse. Designs scalable pipelines, data models, integration frameworks, dashboards, data quality systems, and secure data access processes. Provides technical leadership, governance, interoperability, troubleshooting, documentation, training, and mentorship across public health programs and partner organizations. Analyzes complex datasets to support surveillance, operations, decision-making, and statewide data modernization.

Develops and maintains clinical and research bioinformatics pipelines for cancer genomics, whole-genome and exome sequencing, RNA sequencing, and custom analyses. Builds cloud infrastructure, automates data routing and processing, integrates laboratory and clinical systems, performs variant analysis and quality control, and develops algorithms and AI-enabled tools. Collaborates with clinicians, researchers, laboratory staff, and technical teams. Provides on-call support for pipeline operations and data processing.

Drives revenue expansion across existing B2B SaaS customers through outbound prospecting, account mapping, demos, upselling, cross-selling, contract negotiations, and closing. Manages a high-volume sales pipeline, meets quota and activity targets, identifies new use cases and stakeholders, and partners with Customer Success and Implementation to support adoption and expansion.

Develops fraud detection models using client data, including data cleaning, feature engineering, model tuning, and scalable pipeline development. Collaborates with clients and internal product and research teams, communicates technical findings to diverse audiences, guides project execution, and contributes to generalized product enhancements and fraud prevention solutions.

Leads technical pre-sales for supply chain integration solutions, partnering with Sales to conduct discovery, design ERP/WMS/TMS and trading-partner architectures, run POCs, deliver demonstrations, and respond to RFPs. Advises technical and executive stakeholders on business outcomes and AI opportunities, translates capabilities into measurable ROI, and collaborates with Product, Customer Success, and Professional Services on roadmap feedback and post-sale handoffs.

Manage post-sale customer relationships, onboarding, adoption, retention, renewals, and expansion opportunities for software products. Develop success plans, monitor customer health, conduct executive and quarterly business reviews, and serve as the voice of the customer with Product, Engineering, and Sales. Advise government and defense customers on information advantage products, draft strategic documents, and present operational plans. Requires an active Secret clearance and eligibility for TS/SCI, with occasional travel.

Develop advanced statistical and machine-learning solutions for fraud and other data-intensive business problems. Translate ambiguous needs into hypotheses, experiments, models, and prototypes; shape data strategies; evaluate emerging generative and agentic AI technologies; and partner with Product, Engineering, Analytics, and business teams to operationalize solutions. The role also includes mentoring junior team members and fostering experimentation and innovation.

Leads the engineering team responsible for Airbnb’s globally scalable AirCover insurance platform. Sets technical strategy, architecture, roadmap, and operational standards for configurable insurance products, pricing, enrollment, coverage management, and experimentation. Partners with Product, Data Science, Legal, Insurance, Finance, and Operations to expand across regions and improve attach rates. Manages hiring, mentoring, career development, incident response, and engineering quality while guiding distributed, high-availability backend systems and ML-driven experimentation.

Create and update technical interview guides, question explanations, and structured resources covering SQL, Python, machine learning, statistics, and business cases. Research hiring trends and real interview questions, analyze technical concepts, provide step-by-step explanations, and use AI tools to draft and revise content. Collaborate with editors and subject-matter experts, incorporate feedback, and follow editorial, structural, and SEO guidelines. This is a flexible 20–40 hour-per-week unpaid internship lasting 500 hours over 3–6 months.

Acquire new enterprise customers across AMER for Kimchi, managing the full sales cycle from prospecting and discovery through technical validation, negotiation, close, and value delivery. Build outbound pipeline, sell to technical and financial stakeholders, develop partner relationships, pursue expansions within existing Cast AI accounts, and help shape the go-to-market playbook for a new product and region.

Own the design, development, integration, release, and reliability of tailored AI solutions for enterprise clients. Adapt a core AI platform, deliver client-specific changes to a shared codebase, manage production rollouts, diagnose complex issues, and serve as the primary technical contact for client engineers. The role requires strong Python, software architecture, system design, and experience deploying LLM or agent-based systems used by customers in live environments.

Lead product strategy, roadmap, and delivery for Falcon Complete’s internal MDR tooling, including AI-powered investigation and triage, ticketing, case management, and analyst workbench systems. Partner with security operations, analysts, engineering, and data science teams to translate workflows into product requirements, prioritize automation, improve analyst productivity and response times, define success metrics, and enhance customer outcomes.

Own strategic partner relationships from onboarding through ongoing delivery, serving as the primary point of contact and internal advocate. Lead cross-functional implementations, track commitments, translate partner needs into product and engineering requirements, support roadmap planning, manage escalations, monitor account health, and identify growth opportunities. Establish partner cadences, scalable processes, and centralized request tracking while ensuring partners are prepared to adopt new features.

Own strategic partner relationships, onboarding, implementation, account health, escalations, and recurring partner cadences. Translate partner needs into product and engineering requirements, influence roadmap priorities, and ensure commitments are delivered. Build a scalable partner management function, including request tracking, operating cadences, onboarding processes, and implementation playbooks. Collaborate across Sales, Product, Engineering, Data Science, and Commercial teams to support partner retention, growth, and successful feature adoption.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Leads AI and computational algorithm research, development, clinical validation, deployment, and lifecycle monitoring for oncology diagnostics. Sets foundation model and multimodal AI strategy, prioritizes clinical use cases with Product and clinical stakeholders, manages multidisciplinary algorithm teams, recruits technical leaders, and ensures regulatory-quality data, validation, reproducibility, release controls, and post-launch performance monitoring.

Own and build Fireworks’ customer marketing function, including advocacy, enterprise reference programs, customer stories, peer-to-peer events, co-marketing, awards, and customer-led expansion initiatives. Recruit and develop technical and executive advocates, create compelling proof for sales and marketing, manage customer approvals, and collaborate with Sales, Customer Success, Product Marketing, Developer Relations, Product, and Events. The role requires hands-on strategy and execution in a fast-moving AI infrastructure company.

Research and prototype AI-powered tools for music generation, transformation, remixing, and audio understanding. Build and evaluate machine learning models using Python and modern frameworks, process audio datasets, assess model performance and musical quality, and collaborate with engineering, music technology, product, and creative teams. The intern will also document findings and contribute to responsible AI practices involving artist consent, music rights, and ethical data use.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Lead the technical aspects of enterprise security sales, including discovery, tailored demonstrations, proof-of-value engagements, RFP responses, and architecture discussions. Build relationships with SOC analysts, security architects, and CISOs; translate customer feedback into product improvements; create reusable sales enablement assets; and support early implementation handoffs. The role requires security operations expertise, cloud knowledge, scripting ability, and familiarity with AI and machine learning concepts.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Own the strategy, vision, roadmap, and execution for Proof’s Defend anti-fraud product. Lead fraud investigation and risk prediction tools, customer discovery, strategic planning, GTM, stakeholder alignment, and executive communications. Collaborate with engineering, data science, design, fraud operations, sales, and customer success to define technical requirements, prioritize development, improve onboarding, respond to fraud cases, and deliver data-driven identity and transaction protection solutions.

Build product enablement for global go-to-market teams across AI Agents, APIs, and Creative products. Responsibilities include preparing launch workshops, competitive training, product certifications, onboarding and everboarding content, hands-on demos, role-play simulations, practice environments, and performance measurement. The role partners with product marketing, engineering, research, and enablement teams while using ElevenLabs’ AI technology to create training experiences.