Top Remote Machine Learning Jobs in Raleigh, NC

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead end-to-end healthcare analytics projects, managing scope, planning, execution, client relationships, cross-functional collaboration, risks, and delivery. Provide thought leadership in healthcare analytics and identify business opportunities within client accounts. Required capabilities include quantitative analysis, project management, communication, presentation, SQL/Python/R proficiency, and strategic use of AI tools. Preferred experience includes consulting, cloud analytics platforms, healthcare data regulations, predictive modeling, machine learning, and data visualization.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Own end-to-end design for complex financial product experiences from exploration through launch and iteration. Translate financial workflows into intuitive customer experiences, set high standards for interaction and visual design, partner with product and engineering, use research and data to guide decisions, contribute to design systems, present work clearly, and mentor teammates. Apply AI-enabled tools thoughtfully while maintaining strong human-centered judgment.

Lead implementations of iQueue for Operating Rooms from discovery through configuration, validation, and rollout. Partner with hospital, clinical, operational, IT, and executive stakeholders to optimize OR workflows, scheduling, staffing, utilization, and access. Responsibilities include data analysis, solution design, product configuration, user acceptance testing, change management, risk monitoring, stakeholder alignment, and go-live readiness. Travel to customer sites is required up to 40–50%.

Lead initiatives in designing and building large-scale distributed data systems and ML infrastructure, while collaborating across various engineering teams.

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.

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 a team to analyze, optimize, and grow merchant revenue using analytics and AI. Define strategy, set KPIs, deploy ML solutions, drive go-to-market alignment, execute tests, and deliver actionable insights to executives and cross-functional partners.

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.

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 Enterprise Account Executive drives revenue through high-value contracts, managing complex sales processes and relationships with executive stakeholders in financial services.

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.

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.