Top Remote Machine Learning Jobs in Nashville, TN

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Own the strategy, roadmap, adoption, retention, and monetization of Givebutter Plus CRM, marketing, and reporting products. Lead customer discovery, quantitative and qualitative analysis, product definition, and cross-functional delivery with Design and Engineering. Align stakeholders, define success criteria, incorporate AI, run experiments, and connect product initiatives to business outcomes including SaaS growth and revenue retention.

Lead and transform the Fraud Intelligence Team by designing scalable operating models, QA and measurement frameworks, and talent programs. Drive labeling quality, operational metrics, and fraud prevention impact; convert frontline fraud observations into insights for Product, R&D, Analytics, and GTM; leverage AI, automation, and tooling; and serve as a cross-functional fraud subject-matter expert while developing early-career talent and measurable business outcomes.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Lead strategy and execution for enterprise and mid-market GTM across multi-unit restaurant brands. Drive C-suite engagements, modernize sales motions, scale AI-powered forecasting, partner with Product/Engineering, and represent Toast in industry forums to win large, complex contracts.

Lead strategy and execution to attract, engage, and retain a global community of Pluralsight authors. Build author communications, engagement programs, communities, recognition, and events. Elevate author visibility (Author Fellows), integrate author expertise across Sales, Marketing, Customer Success, Product, and Learning Experiences, and lead a multidisciplinary team to drive measurable business impact.

Lead a global product team building automation-first, AI-assisted investigation and agent-facing tooling for AML, KYC, EDD, and sanctions screening. Define roadmap, metrics, vendor strategy, and regulatory controls to improve investigation quality, cycle time, and operational efficiency while ensuring security, privacy, and auditability.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Lead B2B activation marketing to drive patient starts from commercial referral channels by building scalable systems, attribution frameworks, and repeatable playbooks. Implement MarTech and data-driven strategies, run A/B and incrementality tests, automate campaign execution using AI/ML and LLM tools, and templatize activation campaigns while managing and growing a cross-functional team.

Lead technical project success for enterprise AI infrastructure engagements. Collaborate with engineers, clients, and account leaders to capture requirements, decisions, dependencies, risks, and tradeoffs. Track constraints involving security, compliance, data sovereignty, procurement, and restricted environments. Maintain project visibility, align deliverables with business objectives, resolve issues, support implementation-to-operations handoffs, and identify expansion and renewal opportunities. Translate complex technical concepts for engineering, business, and executive stakeholders.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Lead the vision, strategy, roadmap, and global execution of AI-powered learning and workforce skilling products. Manage and mentor product managers, collaborate with engineering, design, research, marketing, and customer teams, and drive products from customer insight through launch and scale. Build AI-native experiences, improve product adoption and metrics, foster inclusive team practices, and deliver measurable outcomes for millions of learners.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Analyze business processes, data quality, and datasets to identify AI and automation opportunities. Translate business requirements into AI/ML use cases, collaborate on model development, evaluate model performance, and deliver dashboards and reports. The role also supports data validation, AI solution monitoring, emerging technology research, governance, compliance, responsible AI, prompt engineering, and generative AI initiatives while partnering with business, data science, engineering, and product teams.

Conduct prospect research to identify and prioritize high-value individual and institutional donors. Develop donor profiles, relationship insights, predictive machine learning models, graph database capabilities, and agentic AI solutions for prospecting, risk analysis, and pipeline placement. Evaluate internal and external data, optimize analytical models, identify fundraising trends, and translate findings into donor engagement recommendations. Collaborate with technical and development teams while supporting adoption of AI tools and coaching others.

Own the full B2B sales cycle from prospecting and qualification through negotiation, closing, account expansion, renewals, and post-sale relationship management. Build trusted customer partnerships, conduct site visits, represent the company at industry events, and collaborate with leadership, product, and design teams to incorporate customer feedback. The role is remote, focused on U.S. time zones, and requires significant domestic and international travel.

Lead quality initiatives for AI-driven workflows and complex software systems. Design and execute end-to-end test strategies, validate AI-generated outputs, test APIs, webhooks, integrations, and event-driven architectures, and conduct exploratory and manual testing. Partner with Product and Engineering on requirements and release readiness, improve automation coverage, document defects and QA standards, and mentor QA team members while ensuring reliable, compliant, and scalable releases.

Provides advanced internal IT support across identity, devices, software, SaaS, and workplace applications. The role resolves complex employee technology issues, manages support workflows, builds AI-assisted automations and self-service resources, analyzes support metrics, and improves onboarding, access, device, and software processes. It also coordinates with security, infrastructure, endpoint, identity, and enterprise systems teams while supporting distributed employees across time zones.

Own clinical quality for member-facing AI products by partnering with product, engineering, and clinical teams. Design clinical AI evaluations, define quality thresholds and rubrics, assess accuracy, omissions, hallucinations, medication-safety risks, and translate clinical reasoning into prompts and decision logic. Monitor evolving evidence and standards of care, validate product launches, and communicate findings to technical and clinical audiences.

Manage post-sales relationships with a portfolio of customers, primarily financial institutions, to drive adoption and business outcomes from BioCatch’s behavioral biometric solution. Responsibilities include strategic consulting, project coordination, risk and escalation management, customer presentations, quarterly business reviews, roadmap feedback, stakeholder communication, team coaching, statements of work, and identifying expansion opportunities with account managers.

Rotational technology development program providing hands-on experience across cybersecurity, data analytics, technical product and project management, artificial intelligence, application development, and IT infrastructure. Participants receive structured training, mentorship, and leadership development while contributing to healthcare-focused digital transformation initiatives.

Lead design and implementation of Headway's insurance systems (eligibility, pricing, matching, claims). Build data-intensive, ML-powered platforms and automation, mentor engineers, write high-impact code, and improve accuracy and reliability across state and payer complexity to scale insurance-covered therapy.

Leads business analysis, advanced analytics, KPI development, data modeling, reporting automation, and AI/ML initiatives supporting global data-center construction and operational excellence. Builds datasets, dashboards, performance metrics, forecasts, and scenario models; translates complex findings for executives; aligns technology solutions with business needs; and promotes data governance, documentation, process improvement, and analytics maturity across cross-functional teams.

Build and lead Polymarket’s fraud operations function, including investigation workflows, SOPs, case routing, escalation paths, SLAs, QA processes, and reporting. Manage Fraud Analysts and BPO partners, oversee performance and capacity, investigate fraud typologies, identify process bottlenecks, and partner with Product, Engineering, Data Science, Compliance, and Fraud Strategy to improve detection, automation, and prevention.

Owns the roadmap, problem definition, business cases, prioritization, and delivery for a core fintech platform area. Partners with product owners, engineering, data science, marketing, sales, clients, and support to develop scalable, data- and AI-driven capabilities. Monitors adoption, time-to-value, and retention, supports go-to-market activities, incorporates customer feedback, and addresses regulatory and compliance requirements.