Top Remote Machine Learning Jobs in Louisville, KY

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Build and deploy mechanistic, hybrid, and data-driven models for pharmaceutical CMC processes, including drug substance, drug product, packaging, labeling, technology transfer, and manufacturing. Integrate process and experimental data into FAIR data products, analytics, dashboards, and AI-enabled tools. Partner with technical teams and CDMOs to support process understanding, forecasting, risk assessment, regulatory filings, and technology-transfer decisions while owning the digital roadmap and mentoring colleagues.

Senior Product Manager responsible for FinCrime product strategy and delivery across fraud prevention, scams, payment risk, vendor integrations, and cryptocurrency-related products. Owns the full product lifecycle, global feature rollouts, stakeholder management, metrics, regulatory alignment, and customer experience. The role requires hands-on experience with fraud analytics, risk and financial modeling, machine learning, AI tools, APIs, payment systems, and financial-services compliance.

Build and manage technical relationships with LATAM enterprise customers, helping them adopt JetBrains products, overcome technical barriers, and achieve business outcomes. Serve as a trusted technical advisor, customer advocate, and liaison with Support and Product Engineering. Partner with Account Executives and Sales on account strategies and expansions, recommend product enhancements, present product ideas, and maintain expertise in software development technologies.

Leads the architecture, research, development, deployment, and lifecycle management of enterprise AI and machine learning solutions, including generative AI, LLMs, and agentic systems. Partners with data scientists, engineers, IT, business stakeholders, and life sciences experts to deliver reusable, compliant, production-grade solutions across pharmaceutical R&D and corporate functions. Guides AI product strategy, responsible AI, data governance, MLOps, stakeholder adoption, and technical enablement.

Designs, builds, and evaluates agentic AI systems for healthcare workflows such as documentation, coding, denial management, and revenue cycle automation. Develops long-horizon agent behavior, evaluation frameworks, benchmarks, feedback loops, monitoring, and production safeguards. Partners with research, ML engineering, clinical, product, and engineering teams to prototype and deploy reliable, explainable, auditable AI systems using LLMs, machine learning, and agentic technologies.

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.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Build and scale AI-native healthcare products through full-stack applications, backend services, data pipelines, and AI systems. Design, test, deploy, and operate production-ready software, run A/B tests, automate manual workflows, and collaborate with product, operations, and AI/ML teams. The role requires end-to-end ownership of scalable, secure, reliable systems and features, with a focus on performance, data integrity, clean code, and measurable business outcomes.

Conduct applied AI research focused on explainability, reinforcement learning, retrieval-augmented generation, and long-context retrieval. Design, train, and evaluate machine learning models using large-scale healthcare data and distributed infrastructure. Build research prototypes and experimentation frameworks, validate measurable impact, and collaborate with ML Engineering, MLOps, and Product teams to transition innovations into production.

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.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

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.

Drive new enterprise business for Automation Anywhere’s Agentic Process Automation platform. Responsibilities include prospecting, managing sales pipelines, conducting customer presentations and demonstrations, attending industry events, leading proof-of-concept exercises and RFX responses, and coordinating solution design with technical teams. The role requires consultative enterprise software selling, quota attainment, technical communication, and collaboration with sales engineers, partners, and professional services.

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.

Leads strategy, roadmaps, business cases, adoption, and value measurement for enterprise AI products, beginning with AI-driven field-service dispatch optimization. Partners with data science, engineering, operations, finance, marketing, IT, and vendors to deliver measurable business outcomes, manage pilots and rollouts, establish responsible AI practices, and build a scalable AI product function.

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.

Develop technical employment-marketing content that showcases Socure’s engineering challenges, architecture, APIs, machine learning, identity, fraud, and infrastructure work. Interview engineers and technical leaders, write blogs, deep dives, whitepapers, and recruiting copy, and distribute content through developer communities, publications, social channels, and talent platforms. Partner with engineering, recruiting, employer brand, marketing, design, and subject matter experts while maintaining editorial quality and using AI tools thoughtfully.

IT Intern supporting employees with hardware, software, network, and device troubleshooting; assisting with installations, system maintenance, documentation, asset inventories, security monitoring, upgrades, cloud integrations, and process automation. The role also involves improving IT workflows, ensuring data security compliance, and collaborating on technology projects during a full-time summer internship.

Establish and lead the People Analytics function, building trusted workforce data foundations, governance, privacy standards, analytical products, workforce planning capabilities, and AI-enabled decision support. Partner across HR, Finance, IT, and business leadership to deliver executive insights, improve workforce decisions, and scale analytics operations. Recruit and develop the analytics team while ensuring responsible data use, global-local compliance, stakeholder adoption, and alignment with organizational priorities.