Top Machine Learning Jobs in Columbus, OH

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Apply machine learning, artificial intelligence, statistical analysis, and causal inference to large datasets and product analytics. Develop insights and recommendations for recommendation systems and user experiences. Evaluate experiments through planning, monitoring, and post-analysis, build data pipelines and tools, and communicate complex findings to stakeholders to support business decisions.

Build and scale accounting software synchronization solutions connecting BILL’s payment network with third-party accounting platforms. Lead end-to-end software design and delivery, develop scalable AWS microservices, improve platform performance, guide testing and CI/CD practices, resolve customer problems, mentor engineers, and use generative AI to accelerate development.

Develop and maintain internal applications and AI agents using Microsoft Power Platform, APIs, databases, Copilot Studio, and Azure. Support Agile delivery, automated testing, enterprise integrations, conversational AI, intelligent automation, security and compliance standards, performance monitoring, documentation, and cross-functional solution design. The role is remote with occasional travel and offers foundational development experience.

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Leads enterprise healthcare interoperability strategy, operations, connectivity, and strategic partnerships. Establishes operating models, roadmaps, KPIs, governance, and partner ecosystems across EHRs, HIEs, QHINs, payers, providers, and data networks. Translates regulatory and technical requirements into scalable capabilities, supports product and commercial growth, oversees investments and agreements, and builds high-performing teams. Requires executive leadership in healthcare data exchange, interoperability standards, regulatory strategy, operations, partnerships, and technology commercialization.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Own the AI product roadmap, prioritization, accuracy metrics, model and vendor requirements, release planning, and correction feedback loop. Partner with engineering, product, tax, QA, UX, and client success to improve document extraction coverage, accuracy, cost, latency, and filing readiness across K1x products.

Leads the full lifecycle of clinical NGS diagnostic algorithms for oncology and liquid biopsy applications. Responsibilities include algorithm design, analytical validation, design control, regulatory readiness, variant calling, QC metric development, probabilistic modeling, machine learning, and validation pipeline automation. The role serves as technical authority for algorithm changes, supports regulatory submissions, and develops solutions for low-input, fragmented, and ultra-low-frequency genomic variants.

Serves as a permitting and plan review domain expert for engineering, product, design, data, and go-to-market teams. Reviews permit applications, plan sets, submittals, and AI outputs; interprets building codes; identifies missing or noncompliant information; defines approval and revision criteria; labels examples for ML systems; synthesizes jurisdictional requirements; and develops rubrics, checklists, documentation, and benchmarks. Requires approximately 5+ years of hands-on permitting or plan review experience and fluency in applicable building codes.

Leads the Product Acquisition engineering team and drives technology strategy, architecture, software delivery, DevOps maturity, platform adoption, vendor management, and operational excellence. Partners with product managers on roadmaps and business outcomes, ensures engineering standards and production support, and coaches, recruits, and develops engineers and consultants. Requires strong software development, product operating model, project management, stakeholder communication, and technical leadership experience.

Own the vision, strategy, roadmap, business case, licensing, lifecycle policy, deployment models, and go-to-market strategy for GridBeats Device Management. Lead customer discovery, product requirements, release planning, cross-functional delivery, sales enablement, and KPI tracking. Partner with utilities, engineering, commercial, marketing, and global sales teams to deliver secure device-management software for grid operators and industrial customers.

Designs and leads scalable contact center architectures using Zendesk, Amazon Connect, and AWS. Defines modernization strategies, oversees integrations, governance, security, and compliance, and mentors presales specialists. Supports discovery, solution design, demonstrations, proofs of concept, presentations, and technical documentation. Collaborates with business leaders, IT teams, vendors, and partners while driving innovation in omnichannel, AI-enabled customer service technologies.

Conduct research on frontier AI systems and develop datasets, experiments, prototypes, tooling, evaluation frameworks, and reinforcement learning environments. Investigate synthetic and agentic data generation, post-training, model understanding, benchmarks, and evaluation. Collaborate with research, engineering, product, and operations teams to translate findings into scalable AI applications, while contributing to technical publications, open-source work, mentorship, and the broader AI research community.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Design rigorous experiments, train and evaluate models, investigate synthetic and agentic data generation, and translate findings into scalable products. Collaborate with research, engineering, product, and operations teams, communicate technical insights, contribute to publications and open-source work, and mentor peers. This is a fully onsite role based in San Francisco, Palo Alto, or Seattle.

Conduct original research to evaluate, train, and improve frontier AI systems. Design benchmarks, synthetic-data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. Identify research gaps, formulate hypotheses, build experiments and evaluation pipelines, publish findings, and develop new research directions across STEM and AI. The role requires strong Python skills, quantitative reasoning, technical writing, and expertise with modern LLMs.

Conduct original research to evaluate, train, and improve frontier AI systems. Responsibilities include developing STEM benchmarks, synthetic data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. The role involves identifying research gaps, designing rigorous experiments, building evaluation pipelines, publishing findings, and proposing new research directions across reasoning, AI evaluation, data generation, and AI-for-science.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Investigate synthetic and agentic data generation, post-training, model understanding, and model capabilities. Design rigorous experiments, analyze results, and translate findings into scalable products and AI systems while collaborating across research, engineering, product, and operations teams.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Own the strategy, roadmap, packaging, and go-to-market evolution of Pluralsight’s AI, Cloud, and Security learning solutions. Design blended learning programs, define expert perspectives, identify content and lab components, monitor market needs, and use customer and learner data to improve outcomes. Partner with Product, Sales, Marketing, Curriculum, Engineering, and Services teams, support strategic enterprise deals, manage solution benchmarks, and drive adoption, renewals, and revenue growth.

Supports the Data Science and Machine Learning team by analyzing large-scale datasets, identifying anomalies and root causes, developing data validation and normalization methods, and improving the accuracy of derived data products. The intern will evaluate models, translate ambiguous problems into statistical formulations, debug messy data, test hypotheses, and implement maintainable analytical solutions using Python, SQL, and distributed data platforms.

Lead strategy and a portfolio of initiatives to drive advisor conversion and productivity using data, experimentation, and AI. Prioritize opportunities, define success metrics, translate needs into analytics/technology requirements, drive cross-functional delivery, and measure post-launch impact.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.