Top Remote Machine Learning Jobs in Nashville, TN

Lead product marketing for Temporal’s AI initiatives, owning positioning, messaging, competitive intelligence, launches, GTM strategy, content, sales enablement, and market insights. Translate complex AI infrastructure, workflow orchestration, agent, LLM, and human-in-the-loop capabilities into compelling value for technical and business audiences. Partner with product, engineering, sales, demand generation, developer relations, and communications to drive adoption and shape product direction.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

Lead technical strategy for a 50–150+ engineer organization, establish architecture and engineering standards, drive delivery, and partner with executives on technology investments. Embed production-grade generative AI, LLMs, agentic workflows, and ML automation into engineering. Own AI platforms, distributed systems, cloud-native backend architecture, modernization initiatives, microservices migrations, and engineering transformation while ensuring scalable, reliable outcomes.

Leads enterprise-wide AI strategy, governance, portfolio execution, ROI tracking, and responsible AI across multiple healthcare business units. Chairs AI governance bodies, manages initiative gating and investment decisions, coordinates clinical, operational, financial, vendor, compliance, and technology stakeholders, and drives measurable transformation outcomes. The role also leads AI talent development, organizational adoption, vendor discipline, clinical safety, regulatory readiness, and continuous improvement of the governance operating model.

Lead strategy and roadmap for Brain Health & Detection, drive AI/ML-powered detection and anomaly systems, partner with engineering and data science, set success metrics, and lead end-to-end product execution and adoption for scalable service health intelligence.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Owns the lifecycle of AI-powered analytics products supporting clinicians, from requirements and discovery through production and maintenance. Partners with data scientists, engineers, clinicians, and executives to define roadmaps, prioritize trade-offs, establish data contracts and SLAs, validate model results, track performance, and demonstrate ROI. Leads Agile scrum teams and drives cross-functional delivery in complex healthcare environments.

The Product Marketing Manager will execute product launches and support go-to-market initiatives, collaborating with cross-functional teams to develop and deliver messaging and enablement materials targeting both technical and business stakeholders.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Build and deploy generative AI solutions for creative enterprise workflows across image, video, 3D, vector graphics, and design. Partner with customers to identify opportunities, prototype integrations, adapt multimodal models, integrate AI into production systems, diagnose technical issues, evaluate solution quality, and develop reusable deployment patterns. Collaborate with research, product, engineering, and design teams to translate customer needs into scalable technical solutions.

Leads and develops a team of Partner Account Managers while driving partner revenue and strategic growth. Responsibilities include hiring, coaching, territory management, forecasting, Salesforce activity tracking, sales planning, customer and partner engagement, trade show participation, product expertise, and competitive market analysis. The role collaborates with senior leadership and cross-functional teams to achieve sales goals, strengthen customer relationships, and expand business accounts.

Lead the architecture and implementation of AWS cloud-native platforms for fraud detection, risk analysis, and digital intelligence. Design scalable, resilient, secure, low-latency REST and event-driven systems using Java, Kubernetes, Kafka, and SQL/NoSQL databases. Set technical strategy and architecture standards, mentor engineers, conduct reviews, and guide multiple teams. Partner with product, data science, security, and business leaders to embed AI and machine learning capabilities, while improving delivery, observability, compliance, and cost efficiency.

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Build production-ready AI applications, services, APIs, reusable frameworks, and autonomous agents for enterprise quality engineering. Lead architecture and complex technical initiatives, integrate AI into software development and testing workflows, evaluate emerging technologies, and establish AI governance, security, observability, and development best practices. Partner across engineering, product, security, architecture, and data teams while mentoring engineers and shaping long-term AI strategy.

Design and operate large-scale, AI-native cloud infrastructure (especially Kubernetes/AKS). Improve service quality, automate incident prevention/remediation, accelerate engineering productivity, and integrate AI tools into development workflows.

Own the vision, strategy, roadmap, backlog, prioritization, and success metrics for a clinical-trials software product. Lead engineering delivery, make technical trade-offs, and ensure quality, security, compliance, and data integrity in a regulated environment. Partner with customers and cross-functional teams to translate feedback into requirements, deliver incremental software releases, and guide teams through influence. Use analytics and responsible AI to improve product outcomes and team effectiveness.

Develop new enterprise and upper mid-market business, manage client accounts, and serve as a trusted consulting advisor. Prospect and qualify leads, collaborate with technical teams to create proposals and close SOW contracts, identify business improvement opportunities, build channel partnerships, maintain CRM records, and support localized marketing activities. The role is remote within specified target markets, includes up to 10% travel, and has no direct reports.

Lead partner analytics by building scalable dashboards, forecasting and ROI models, and experimental designs to evaluate and optimize partner performance. Collaborate with Product, BD, Marketing, and Data Engineering to improve pipelines, translate insights into recommendations, and mentor analysts to scale partner growth.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Designs and maintains high-volume HL7 and FHIR ingestion pipelines, secure Epic and hospital integrations, data validation systems, and compliant healthcare data exchanges. Builds enterprise SaaS microservices using ReactJS, Python, and AWS services; optimizes distributed and hybrid-cloud environments; leads testing, code reviews, production troubleshooting, architecture documentation, and customer communications. Preferred experience includes Epic App Orchard, healthcare integration engines, clinical workflows, GenAI/ML, DevOps, data pipelines, and NoSQL.

Research and improve real-time conversational AI for voice drive-thru ordering. Responsibilities include advancing speech, intent, turn-taking, voice activity detection, and order-accuracy models; curating and generating data for rare scenarios; building statistically rigorous benchmarks; evaluating audio and conversational quality under real-time constraints; and translating research into production improvements with applied AI and backend teams.

Own and improve the end-to-end AI engine powering real-time voice drive-thru agents. Design conversational and order-taking logic, address turn-taking and noisy audio challenges, build evaluation infrastructure, and ensure accurate recommendations, substitutions, pricing, and POS integrations across restaurant brands. The role involves researching and deploying new AI techniques, training and productionizing machine learning models, and converting ambiguous quality issues into measurable improvements.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.