Top Remote Machine Learning Jobs in Cincinnati, OH

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Leads enterprise product evaluations, implementations, integrations, and adoption initiatives. Manages cross-functional projects involving AI, automation, system modernization, and third-party platforms. Translates business requirements into implementation plans, develops business cases and ROI analyses, coordinates vendors and stakeholders, prepares executive communications, assesses risks, and supports organizational change and user adoption.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Lead phData’s life sciences commercial strategy and solutions business across market access, pricing, patient services, commercial operations, omnichannel, payer analytics, and gross-to-net. Develop go-to-market offerings, accelerators, proposals, strategic pursuits, thought leadership, and account expansion opportunities. Advise executive clients while partnering with data, engineering, machine learning, and platform teams to create scalable, technically feasible solutions.

Provides strategic finance leadership to venture-backed AI and SaaS startups. Responsibilities include monthly close oversight, budgeting and forecasting, capital planning, treasury management, board reporting, investor updates, fundraising, M&A support, cap table analysis, revenue cycle management, cash flow planning, controls, audit readiness, and advising founders on financial strategy.

Build edge-focused full-stack product experiences integrating machine learning and autonomy for frontline defense users. Collaborate with designers, infrastructure teams, product teams, and ML engineers to create intuitive applications, efficient edge-deployed application stacks, and internal modeling APIs. Develop maintainable software using strong engineering practices, including design documents, code reviews, and style guides.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Leads enterprise product and technology integrations, cross-functional projects, AI and automation initiatives, business analysis, vendor coordination, executive communications, and change adoption. Develops requirements, implementation plans, business cases, risk strategies, and executive reporting while ensuring initiatives meet scope, schedule, compliance, and organizational objectives.

Lead the data science function, owning machine learning strategy, predictive models, healthcare analytics, DBT data marts, and executive dashboards. Analyze claims, eligibility, pharmacy, EMR, HIE, clinical, and operational data to identify high-risk patients, evaluate clinical interventions, and improve patient and provider engagement using AI and LLMs. Collaborate with clinical, operations, and technology teams while leading data science staff in a fast-growing healthcare organization.

Manage client relationships throughout implementation and ongoing support, serving as the primary client contact. Oversee KPIs, operational metrics, strategic planning, project execution, and client goal attainment. Translate client needs into platform opportunities, represent customer feedback to Product and Engineering, and develop case studies and success materials. The role requires healthcare client success or account management experience, strong communication, project leadership, and knowledge of medication management and healthcare quality measures.

Design, build, and deploy production-grade Generative AI applications and multi-agent solutions. Develop RAG pipelines, integrate commercial and open-source LLMs, fine-tune models, and build scalable Python APIs. Optimize applications for performance, latency, cost, reliability, and scalability. Implement monitoring, containerization, CI/CD, and cloud deployments while collaborating with engineering, architecture, AI/ML, and product teams.

Leads the strategy, roadmap, and execution of search, ranking, matching, and recommendation products for a two-sided marketplace. Oversees algorithmic performance, resource allocation, experimentation, metrics, executive communication, and cross-functional alignment across consumer and enterprise experiences. Coaches product managers and establishes scalable operating rituals while connecting technical improvements to engagement, retention, customer value, and marketplace growth.

Builds and operates production AI, machine learning, generative AI, and agentic systems. Responsibilities include designing ML architectures, developing models, implementing MLOps and LLMOps pipelines, monitoring production performance, establishing AI governance, and partnering with data engineering teams. The role sets technical standards, mentors engineers, drives scalable Databricks and Azure solutions, and influences architecture across multiple project and product teams.

Develop NLP, LLM, and machine learning pipelines to abstract clinical concepts from structured, unstructured, and multimodal healthcare data. Query healthcare source systems, curate and validate AI-ready datasets, support patient phenotyping and real-world evidence analyses, and deliver HIPAA-compliant data products. Collaborate with product and engineering teams, communicate findings to technical and clinical audiences, and improve data quality for customer AI applications. The role is remote with occasional quarterly travel.

Own customer-facing AI and data engagements from discovery through production launch. Define business value, MVPs, success criteria, and product requirements; prioritize engineering backlogs; lead technical discovery, architecture reviews, agile delivery, testing, production readiness, launch, adoption, and strategic handoff. Build C-suite relationships, translate business needs into technical requirements, resolve delivery risks, and identify future high-value opportunities.

Lead Cure from company formation through product-market validation and seed financing. Define the market wedge, secure enterprise design partners, shape an AI-native recovery product, establish data and evaluation loops, recruit the founding team, build the company narrative, and lead fundraising. The role requires enterprise AI judgment, commercial credibility, consumer-finance and regulatory understanding, and founder-level ability to create a new category.

Founding CTO will architect and build Cure’s AI-native customer engagement and recovery platform. Responsibilities include developing conversational agents, decisioning and personalization systems, evaluation and feedback loops, enterprise integrations, and secure, explainable infrastructure. The CTO will work with customers, lead product and engineering execution, recruit early technical talent, and establish the organization needed to scale a reliable platform for regulated industries.

Research and prototype transformer-based and graph-based foundation models using customer, transaction, and merchant data. The intern will design and train models, run rigorous experiments, evaluate results, and develop working prototypes. Responsibilities include collaborating with Product, Engineering, Analytics, and Applied Science teams to improve customer experience, operational efficiency, and business outcomes.

Write and adapt technical and non-technical content across social media, blogs, founder communications, and company announcements. Translate research papers and technical findings into accessible writing, maintain distinct brand and founder voices, verify claims, collaborate with stakeholders, repurpose content across channels, and independently manage deadlines for a part-time contract.

Design and implement automated testing frameworks for data platforms, pipelines, distributed processing systems, and machine learning workflows. Develop strategies for data quality, schema integrity, drift, model accuracy, bias, regression, performance, and resilience. Investigate production failures, conduct exploratory testing, embed continuous validation into delivery processes, collaborate across engineering and data teams, and mentor junior SDETs.

Conduct computational discovery and optimization of rare-earth-lean and rare-earth-free permanent magnets. Execute DFT and multiscale simulations, develop machine-learning workflows and interatomic potentials, generate high-throughput materials data, and connect atomistic predictions to microstructure and manufacturing processes. Collaborate with experimental teams to validate models, contribute to publications, patents, project planning, and technical presentations, and support junior researchers.

Leads the company’s global Revenue Operations organization, including Field Operations, Marketing Operations, Data Analytics, forecasting, GTM planning, territory and quota design, revenue systems governance, and AI-enabled automation. Partners with the CFO, CRO, CMO, and Board to improve forecast accuracy, pipeline health, sales productivity, retention, data reliability, and Salesforce and RevTech execution. Builds and develops a high-performing organization supporting growth from approximately $550 million to more than $1 billion in ARR.

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.