Top Senior Machine Learning Jobs in Columbus, OH

Lead post-sales success for enterprise SaaS customers, drive adoption, monitor account health and model performance, manage escalations, run QBRs, partner cross-functionally to ensure renewals and expansions, and deliver measurable business outcomes while advising on identity verification and fraud detection solutions.

Performs advanced physical and remote insurance premium audits for business clients. Reviews business records, interviews owners and managers, investigates exposures, documents findings, prepares written audit reports, schedules assignments, communicates results, and submits completed work for quality review. The auditor must manage deadlines independently, correct returned audits, maintain accurate records, and use Microsoft Office applications. This remote role requires reliable transportation, a valid driver’s license, strong analytical and communication skills, and at least five years of premium insurance auditing experience.

Leads the company’s adoption of AI, analytics, automation, and business intelligence. Builds AI-powered workflows, Excel solutions, natural-language data interfaces, dashboards, forecasts, predictive analytics, and automated reporting using Snowflake, SQL, Tableau, and related tools. Partners across business functions to identify opportunities, improve data quality, automate repetitive processes, and deliver scalable decision-support solutions. Establishes responsible AI practices and advances an AI-first analytics culture.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Develop, prototype, and integrate thermal control algorithms into embedded and software platforms. Create system simulation models, support automated and hardware-in-the-loop testing, lead validation and test campaigns, assist firmware integration, document designs and user guides, and support global product implementation and field testing.

Own strategic enterprise accounts across expansion and new-logo sales motions. Build executive relationships, develop account plans, run account-based outbound, navigate complex buying committees, deliver consultative demonstrations, negotiate contracts, and close high-value SaaS deals. Partner with Customer Success, Business Development, and Marketing while maintaining Salesforce pipeline and forecasts. Success requires consistently meeting quota through longer-cycle, multi-stakeholder deals and contributing to the evolving enterprise sales playbook.

Drive new-account acquisition and cloud migrations for AI and infrastructure solutions. Generate pipeline through global outbound prospecting, execute multichannel outreach, manage the full enterprise sales cycle, and close large cloud, infrastructure, and data center deals. The role requires deep knowledge of AI computing, cloud infrastructure, power capacity, cooling systems, financial modeling, and B2B enterprise net-new cloud sales. Candidates work from Akamai’s San Francisco office and collaborate across time zones.

Own technical product marketing for Bloomreach’s APIs, integrations, and agentic AI platform. Translate platform capabilities into positioning, use cases, partner launch strategies, customer storylines, sales enablement, and go-to-market materials. Collaborate with Product, Engineering, Solutions Engineering, partners, sales, and technical buyers. Define launch metrics, coordinate milestones, communicate product value, and evaluate post-launch results.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Own the strategy, requirements, evaluation, and execution of AI agent products and systems. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define evaluation frameworks, drive prioritization, and ensure AI features are understandable, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions and reliable real-world workflows.

Own the strategy, requirements, evaluation, and reliability of AI agent products. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define offline and online evaluation frameworks, drive product execution, and ensure AI features are correct, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions, roadmaps, and rapid iteration.

The Senior Product Architect will design security products, collaborating with product management and engineering teams, focusing on architecture and functionality for high-level product features.

Lead the Data Sources product roadmap for document parsing, fraud detection, and bank aggregation. Drive metrics (parse accuracy, fraud catch rate, connection success, SLAs), write specs, engage clients, mentor PMs/engineers, and represent the platform externally. Prioritize document types, extraction models, reliability, and scalability to improve conversion, client satisfaction, and operational efficiency across mortgage, tenant screening, and government use cases.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

Lead development of safety frameworks and V&V for end-to-end autonomous robotics, perform risk assessments and safety reviews, derive technical safety requirements, and coordinate cross-functional teams to monitor fleet performance and deploy scalable autonomy.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Director of AI will lead AI and machine learning initiatives, develop AI architecture for humanoid robots, oversee production-ready AI systems, and manage team growth and budget.

Develop and expand software products and platforms across backend, frontend, cloud, AI, and machine learning systems. Build efficient, reliable, and testable code; collaborate with business partners to launch features; influence product and engineering decisions; mentor team members; and help shape team culture and product vision in an agile environment.

Design, implement, and operate key components of a high-throughput link harvesting and evaluation pipeline. Lead architecture and technical decisions, mentor junior engineers, adopt AI/ML (LLMs) in production, ensure system reliability and low impact on other services, and maintain high code quality with CI/CD and production support.

Own product strategy, vision, roadmaps, backlogs, and delivery for enterprise SaaS products serving financial services organizations. Translate strategy into requirements and user stories, lead Agile ceremonies, engage customers, monitor product metrics, manage delivery risks, and align portfolio companies with leadership and cross-functional teams. Identify and deliver AI-enabled capabilities, workflow automation, analytics, and intelligent business solutions.

Serve as the primary product contact embedded with health system clients to discover workflows, design AI-powered solutions, scope pilots, and lead 0→1 deployments. Translate clinical and administrative workflows into product requirements, drive UAT and go-live, measure business success metrics, and surface field insights into the product roadmap. Travel to client sites for discovery and deployment support and collaborate with ML engineers, FDEs, and solution teams to ensure measurable ROI and expansion.