Top Remote Machine Learning Jobs in Columbus, OH

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Build tutorials, reference solutions, and workshop content for ML inference, serving, and fine-tuning on AMD Instinct GPUs. Evaluate models and frameworks, develop platform examples, support technical events, collaborate with marketing on technical content, gather developer ecosystem insights, and contribute fixes to upstream projects.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Own the end-to-end product strategy and lifecycle for AI research products, from model development and user research through launch, pricing, and go-to-market. Partner with research, engineering, design, and business teams to define requirements, ship products, analyze metrics, run experiments, and prioritize features. The role requires strong technical or quantitative foundations, product management experience, SQL and Python fluency, knowledge of machine learning, and experience taking products from zero to one.

Develop, deploy, troubleshoot, and optimize AI/ML components in secure production applications. Responsibilities include data preprocessing, feature engineering, LLM and RAG integration, prompt engineering, model evaluation, LLMOps monitoring, API integration, observability, and defect resolution for government and defense-adjacent customers.

Build, optimize, debug, and evaluate infrastructure for generative AI and large language models. Responsibilities include accelerator kernel development, performance profiling, distributed workload debugging, high-throughput inference serving, benchmarking, reference solution development, AI-generated solution evaluation, technical rubric creation, and analysis of ML infrastructure, compiler, accelerator, and model-serving bottlenecks. Collaborate with researchers and subject matter experts to improve AI training and evaluation data.

Lead the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable model-serving infrastructure. Establish engineering standards, guide complex initiatives, improve reliability and cloud cost efficiency, and partner across engineering and data teams. The role also includes mentoring engineers, managing production operations, and developing AI-assisted automation tools.

Build, fine-tune, evaluate, and deploy computer vision and multimodal machine learning models in production. Develop scalable data pipelines, evaluation frameworks, real-time processing systems, and cloud infrastructure with monitoring and automated scaling. Improve image processing, barcode decoding, and OCR capabilities while partnering with engineering and fulfillment teams. Contribute through code reviews, documentation, testing, technical leadership, and mentorship.

Assist with developing, training, validating, and monitoring machine learning models and production ML systems. Responsibilities include preparing datasets, writing Python scripts, building feature engineering pipelines, evaluating models, investigating errors, documenting experiments, supporting deployment and inference testing, and contributing to responsible data-handling practices. Collaborate with experienced engineers and product teams while learning practical ML engineering workflows, software testing, version control, and production monitoring.

Designs, builds, and deploys production machine learning systems for search, ranking, discovery, recommendations, and personalization. Responsibilities include semantic and hybrid retrieval, embeddings, data pipelines, search infrastructure, evaluation, monitoring, experimentation, performance optimization, and privacy-aware access controls. The role partners with product and engineering teams to integrate intelligent capabilities across healthcare, commerce, clinical, pharmacy, patient, and merchant applications, while mentoring engineers and owning initiatives through deployment and continuous improvement.

Build and deploy production machine learning and recommendation systems for personalized healthcare platform experiences. Develop ranking models, feature pipelines, preference representations, model-serving APIs, evaluation workflows, and experimentation strategies. Monitor model quality, latency, reliability, and data issues while improving performance and handling cold-start scenarios. Collaborate with product, backend, frontend, and data teams to own personalization features through deployment. Ensure privacy-conscious processing, tenant boundaries, authorization, and approved clinical workflows.

Build and improve machine learning-powered search and discovery systems, including semantic and hybrid retrieval, ranking, query processing, indexing, APIs, and RAG support. Develop evaluation datasets and relevance metrics, analyze search quality, optimize latency and scalability, and implement monitoring and testing. Ensure permission-aware retrieval across tenant boundaries and collaborate with product and engineering teams to deliver reliable search features from development through continuous improvement.

Build and operate production infrastructure for machine learning systems, including model-serving APIs, batch and real-time inference, deployment automation, artifact management, monitoring, data pipelines, and reliability tooling. The role integrates ML services with backend systems, improves scalability and performance, implements security and tenant isolation, troubleshoots incidents, and establishes reusable deployment and operational patterns in a healthcare-related environment.

Lead the design and production operation of Navanta’s AI platform, including RAG, retrieval, model serving, MCP tool orchestration, text-to-SQL, evaluation, and guardrails. Build auditable, secure AI systems with PII protection, prompt-injection defenses, grounded responses, and governed metric selection. Partner with data, platform, and product teams while optimizing model quality, latency, cost, and reliability for regulated banking environments.

Own enterprise customer relationships after implementation, driving BigPanda platform adoption, measurable IT operations outcomes, renewals, and expansion. Lead technical customer sessions, health checks, executive reviews, adoption roadmaps, and value conversations. Partner with account teams on renewal risk, upsell opportunities, and technical proof-of-value engagements. Act as the customer’s internal advocate while guiding integrations, troubleshooting, and platform configuration.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

Own the roadmap and product decisions for data science products powered by NEXUS. Partner with data scientists, engineering, research, design, and applied AI teams to build self-service workflows, AI agents, MLOps capabilities, and predictive model monitoring. Conduct customer discovery, define requirements and success metrics, prioritize roadmap decisions using data and user evidence, and guide products from discovery through launch and iteration.

Lead product strategy for integrated hardware and software ecosystems serving the public sector. Oversee cameras, IoT, robotics, software, and AI/ML products across their full lifecycle, from concept and NPI through manufacturing, deployment, and end-of-life. Manage and scale the product organization, guide roadmap and P&L decisions with data, develop go-to-market strategies, and align engineering, supply chain, commercial teams, executives, and the Board.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Lead architecture and technical strategy for a remote engineering organization building a React, Node.js, PostgreSQL, and AWS platform. Mentor engineers, establish engineering standards, drive JavaScript-to-TypeScript migration, design ETL and AI/LLM integration systems, resolve technical debt, and oversee technical design reviews, production support, incident response, hiring, and delivery. Partner with product, clinical, and business stakeholders to build scalable, reliable software.

Leads enterprise adoption of AI, machine learning, generative AI, and intelligent automation across federated engineering teams. Defines reference architectures, standards, reusable patterns, and Responsible AI governance while advising delivery teams on secure, resilient, scalable implementations. Influences senior stakeholders, promotes AI-assisted engineering practices, establishes validation standards, and builds communities of practice to improve consistency, delivery speed, and operational readiness.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Own projects from prototyping through production, conduct experiments, explore large-scale data, and collaborate with product, engineering, analysts, and account teams to improve attribution and marketing ROI.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Secure software and cloud environments through threat modeling, code review, vulnerability remediation, security automation, detection engineering, and attack simulations. The role partners closely with Engineering, manages Snyk and Docker Scout findings, integrates security into CI/CD, coordinates third-party penetration testing for SOC 2 and HITRUST, and helps define secure architecture for mobile, data lake, AI, and production cloud systems.