Top Senior Machine Learning Jobs in Columbus, OH

Deploys, integrates, and scales enterprise Generative AI solutions across Azure environments. The role develops LangGraph agentic workflows, fine-tunes and evaluates LLMs, reduces hallucinations, integrates retrieval systems and APIs, and collaborates with customer engineering teams. Responsibilities also include requirements documentation, process mapping, automation engineering, software architecture, and communicating business value.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Leads customer-facing development of AI agent systems, multi-agent workflows, BI and ML pipelines, and enterprise data governance solutions. Partners with Fortune 500 stakeholders to translate business and data challenges into production-grade technical solutions, develops reusable delivery patterns and internal tooling, influences product roadmaps, conducts competitive analysis, and mentors engineers and technical teams.

Serve as strategic commercial and technology legal advisor, drafting and negotiating software, SaaS, licensing, data, customer, and vendor agreements. Advise on AI products, governance, responsible AI, privacy, intellectual property, regulatory compliance, product launches, and legal risk mitigation. Partner with Sales, Product, Engineering, and executive teams while supporting international and broader legal initiatives. Use legal technology and AI tools to improve legal service delivery.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Own the end-to-end machine learning and LLM lifecycle, taking models and agentic AI workflows from experimentation into reliable, scalable production. Design MLOps infrastructure, serving, orchestration, CI/CD, evaluation, observability, and GPU optimization. Build self-service tooling and establish LLMOps standards while partnering with engineering and business teams. The role is fully remote and involves up to 10% travel.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Conduct end-to-end analytics and causal investigations to improve LLM and GenAI products, identify customer friction, optimize model performance, automate reporting, and deliver actionable insights. Develop SQL and Python solutions across AWS and on-premises environments, collaborate with business and technology teams, investigate data and production issues, and support agile product development. Apply regression, clustering, classification, controlled experiments, causal inference, NLP, and machine learning techniques.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Founding Product Manager responsible for defining and launching Hightouch’s Agentic Personalization product. The role includes customer discovery, product strategy, agent workflow and approval design, roadmap sequencing, technical decision-making, revenue alignment, competitive positioning, customer calls, and go-to-market collaboration. The PM will work cross-functionally with engineering, design, sales, customer success, founders, and executives to establish product-market fit and scale a new enterprise SaaS offering.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.

Develop applied AI research prototypes and systems for agentic marketing applications, focusing on generative image and video, brand asset creation, data workflows, and autonomous evaluation. The role combines probabilistic and quantitative reasoning, backend system architecture, product intuition, experimentation, and customer-focused product development. Responsibilities include building proofs of concept, improving AI-generated media realism and brand alignment, and architecting agentic systems.

Design and launch 0-to-1 AI-powered marketing tools for Hightouch’s Agents product suite. Responsibilities include rapid prototyping, customer research, synthesizing usage data and feedback, shaping product strategy, and collaborating with designers, engineers, and product managers. The role requires strong visual, UI, and interaction design skills, comfort with ambiguity, curiosity about AI, and rapid iteration in a remote-first environment.

Manage complex customer issues, triage support activity, and coordinate escalations across Client Engagement, Product, Engineering, and project teams. Analyze tickets and feedback to identify trends, risks, process gaps, and product opportunities. Improve support operations through documentation, dashboards, workflows, routing logic, playbooks, and feedback loops. Serve as a customer advocate while supporting strategic initiatives and strengthening handoffs, prioritization, and operational decision-making.

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.

Leads the data science platform and machine learning strategy, building data marts, predictive models, MLOps infrastructure, and analytics capabilities. Analyzes healthcare claims, clinical, workflow, and operational data to risk-stratify patients, assess clinical program impact, support dashboards, and improve patient and provider engagement. Collaborates with clinical, operations, finance, product, executive, and technology teams while serving as a trusted analytics expert for a business workstream.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Leads complex enterprise customer engagements as a technical authority for Databricks data and AI solutions. Drives architecture, platform adoption, consumption strategy, competitive positioning, and executive relationships. Designs large-scale data, streaming, migration, governance, and AI/ML solutions; orchestrates cross-functional teams; mentors other solutions architects; and contributes to technical thought leadership. Requires advanced coding, enterprise data architecture expertise, Databricks proficiency, strategic account leadership, and executive communication.

Lead product-focused data science and machine learning initiatives, including exploratory analysis, production model development, evaluation frameworks, generative AI, graph-based machine learning, recommendation systems, and personalization. Collaborate with engineering, product, and business teams to deliver high-impact solutions, influence strategy, and communicate insights to technical and non-technical stakeholders. The hybrid role requires four days per week in the Greater Boston area.

Senior legal executive leading institutional bank partnerships, complex commercial and program agreements, consumer lending regulatory strategy, data protection, intellectual property, and AI underwriting compliance. The role advises executive and cross-functional teams, manages bank due diligence and risk requirements, and translates legal and regulatory considerations into scalable business strategies.

Leads the creation and global scaling of an AI Data Operations practice. Responsibilities include defining service strategy, developing AI data and evaluation solutions, originating and closing client engagements, establishing quality and governance frameworks, building strategic partnerships, providing thought leadership, and enabling scalable managed-service delivery. The role combines AI expertise, consulting, sales, solution design, commercial leadership, and global operations.

Design and build a production-grade AI memory and retrieval engine for personalized, privacy-preserving LLM experiences. Responsibilities include developing multi-tier storage and hybrid retrieval pipelines, memory extraction and classification, adaptive updates, evaluation frameworks, and reliability safeguards. The role researches advanced memory architectures, supports agentic workflows and tool integrations, collaborates with product management, and owns production operations. Strong software engineering, Python, NLP/ML, vector database, LLM orchestration, privacy, and RAG expertise are required.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.

Designs and manages AWS cloud infrastructure and Amazon EKS platforms using GitLab CI/CD and Infrastructure as Code. Builds ETL workflows and scalable data pipelines for analytics and AI workloads, supports MLOps/AIOps and FinOps capabilities, troubleshoots Linux systems, and improves platform performance, security, scalability, and availability.

Provides senior functional and technical leadership for NetSuite and connected financial systems. Partners with Finance, Accounting, and technology teams to design processes, configure NetSuite, manage integrations, protect close-cycle continuity, support production incidents, implement controls, coordinate releases, and deliver testing and documentation. The role also leads automation initiatives, vendor engagement, solution lifecycle delivery, and mentorship while supporting accounting, compliance, reporting, and multi-entity financial operations.