Top Machine Learning Jobs in Nashville, TN

Leads an engineering team building and scaling customer-facing integrations for agentic AI products. Responsibilities include setting technical direction, balancing customer delivery with reusable tooling and platform investments, ensuring reliability and observability, improving developer experience, collaborating cross-functionally, recruiting, and coaching engineers. The role requires customer empathy, strong engineering leadership, technical problem-solving, and experience with production systems or integrations.

Assess AI-enabled products and software against regulatory requirements and standards, supporting conformity assessments, CE marking, certification, and client guidance. Review technical documentation, mentor colleagues, develop assessment procedures and tools, contribute to AI testing methodologies, and support accreditation, quality, and continuous improvement activities. The role requires practical AI/ML experience, regulatory knowledge, strong analytical skills, and collaboration across global multidisciplinary teams.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Lead large, cross-functional programs for Netflix Cloud Infrastructure and AI Platform. Shape multi-year strategy, drive migrations and platform adoption, structure ambiguous technical problems into executable plans, execute end-to-end delivery, manage dependencies and risks, influence investment and architecture, and enable ML practitioners to build, train, deploy, and operate models at scale.

Build and operate scalable, highly available ML/LLM serving and compute infrastructure. Own long-term platform vision, deliver zero-to-one projects, partner cross-functionally, optimize performance and capacity, and support production ML models at scale.

Lead design and execution of scalable experimentation frameworks for Netflix Ads. Develop auction-based, biddable media models, solve cold-start measurement problems globally, apply causal inference and ML, partner with product and engineering, and translate analytic results into strategic recommendations to optimize marketplace dynamics and member experience.

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Lead design, execution, and optimization of scaled lifecycle campaigns across email and in-product channels. Use marketing automation, behavioral data, and ML techniques to personalize journeys, drive activation, revenue, retention, and cross-sell. Analyze performance, generate insights, and collaborate with Growth, Product, Engineering, Data Science, Analytics, Sales, and Marketing stakeholders.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The Director of Computer Vision will lead technical vision, research, and execution in developing scalable AI-driven computer vision solutions, managing teams, and enhancing performance metrics.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

Lead and build a team that drafts and submits state-specific rating rules, underwriting guidelines, and forms; manage filing execution via SERFF, handle objection responses, coordinate cross-functional stakeholders, establish operating rhythms, improve documentation and controls, and coach staff to scale filing expertise and regulatory execution.

Lead technical strategy and execution for production clinical AI across a portfolio: set AI direction, ensure regulatory and governance alignment, partner with product and engineering, improve model evaluation/monitoring and production reliability/cost/latency, promote reusable model patterns, and hire/coach data science leaders to scale clinical AI capabilities.

Serve as the technical post-sales contact for enterprise customers: drive adoption of JetBrains products, manage technical account relationships, advise on architecture and tooling, advocate customer needs to product and support teams, recommend enhancements, support product evaluations, and collaborate with sales to grow accounts. Requires travel (~25%) and bilingual English with Portuguese and/or Spanish.

Serve as the technical point of contact for enterprise accounts, driving adoption of JetBrains products, advising on architecture and tooling, advocating customer needs to product and support teams, enabling account expansion with Sales, and delivering product demonstrations and technical guidance. Travel roughly 25% required.

Lead go-to-market and partner development for Personal Loans and Point-of-Sale products. Own end-to-end enterprise sales, build C-level relationships, close complex deals, and coordinate cross-functional teams to launch AI-driven consumer lending integrations and scale market presence.

Lead net-new enterprise sales into Fortune 500 and large mid-market accounts building AI/ML systems. Prospect and create outbound pipeline, run long consultative sales cycles, orchestrate pilots and phased rollouts, influence technical and executive stakeholders, and partner with solution architects to close complex, high-value deals.

Lead enterprise data security architecture to protect sensitive data across its lifecycle. Define strategies for encryption, key management, tokenization, masking, DLP, classification, database security, and Zero Trust. Advise execs and engineering on secure design patterns for data platforms (e.g., Starburst), cloud/on-prem systems, APIs, data pipelines, and AI/ML workloads. Develop roadmaps, evaluate technologies, mentor staff, and drive secure-by-design adoption across the organization.

Author and verify rigorous multiple-choice computer science assessment items across many CS domains to create gold-standard benchmarks for AI research. Produce correct answers, nine plausible distractors, solution explanations, difficulty labels, and authoritative references. Review and edit questions for technical accuracy, clarity, and reproducibility. Contribute asynchronously as an independent contractor, ~10+ hours/week, fully remote.

Define and execute the product roadmap for AI-driven on-chain monitoring tools to detect blockchain threats. Collaborate with engineering, research, and stakeholders, analyze market and customer feedback, prioritize features, ensure timely delivery, and maintain transparent communication across teams.

Lead a global manufacturing engineering organization to standardize processes, drive lean continuous improvement, deploy automation and Industry 4.0 solutions (cobots, ML), and ensure safety, traceability, and zero-defect customer quality across international sites.

Lead security engineering for an AI-native cybersecurity product: architect product security, build and evolve operational security and incident response, collaborate with ML engineers to integrate security into AI agents, implement protections and automation, and represent Cogent externally through writing and speaking.