Top Machine Learning Jobs in Charleston, SC

Own the vision, strategy, roadmap, discovery, delivery, and continuous improvement of Rezilient’s member experience products. Partner with clinical operations, engineering, design, customer success, and sales to ship accessible, engaging healthcare experiences. Define requirements, prioritize backlogs, establish product metrics, ensure healthcare compliance, and help integrate AI into care delivery. The role requires experience scaling consumer health products, building AI or machine learning features, and operating effectively in a regulated startup environment.

Provides strategic legal counsel on clinical trials, commercial biopharma services, healthcare regulation, privacy, data protection, and EU employment matters. Advises on GDPR, HIPAA, clinical data use, cross-border transfers, privacy impact assessments, contracts, workforce initiatives, works councils, and regulatory developments. Partners with clinical, commercial, HR, compliance, technology, and senior leadership teams while supporting privacy program implementation and risk management.

Leads credit risk analytics for Prosper’s Credit Card business, overseeing underwriting, portfolio, pricing, credit line, fraud, and credit risk strategies. Uses machine learning, AI, and advanced analytics to manage growth while controlling losses within risk appetite. Presents recommendations to executives, the Credit Risk Committee, and investors; monitors performance and emerging trends; evaluates vendors and alternative data; and collaborates across Capital Markets, Product, Engineering, Legal, Compliance, Operations, and Marketing. Also builds and mentors high-performing credit risk teams.

Own and grow strategic Financial Services accounts through executive relationship management, consultative selling, renewals, and expansion. Lead commercial strategy, SOWs, negotiations, QBRs, delivery governance, escalations, and value realization. Coordinate delivery, sales, solutions, cloud, and Databricks partners while managing scope, budgets, timelines, margins, retention, profitability, and customer satisfaction. Translate Databricks, data engineering, analytics, ML, AI, and cloud transformation capabilities into measurable business outcomes.

Leads regional life science enterprise sales by developing pipeline, closing complex deals, expanding accounts, and building executive relationships across health plans, health systems, biotech, and pharmaceutical organizations. The role requires consultative selling of AI/ML healthcare solutions, cross-functional collaboration, CRM management, contract negotiation, and trusted-advisor engagement with C-level decision-makers. This remote position requires 25–50% travel.

Drive enterprise sales of Snowflake’s AI and machine learning solutions by generating pipeline, achieving revenue targets, developing territory strategies, and executing tailored sales plays. Engage executives and technical decision-makers, manage complex sales cycles, collaborate with account executives, sales engineers, field CTOs, and cross-functional teams, and provide customer insights to inform product strategy. Track sales performance and guide customers through AI/ML solution adoption, implementation, and deployment.

DoD SkillBridge Fellows gain practical experience through assigned projects, collaboration with experienced professionals, and exposure to operational processes, technologies, and mission objectives. Opportunities span telecommunications, acquisition and procurement, technology, accounting, finance, and other areas. Participants are compensated by their military employer during the fellowship and may be considered for full-time employment afterward based on performance and organizational needs.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

Develop and deploy AI solutions from problem framing through production. Responsibilities include identifying AI opportunities, leading MVP development, building data pipelines, experimenting with generative LLMs, fine-tuning NLP models, and delivering scalable features. The intern will collaborate cross-functionally, communicate technical impact to business audiences, and work with cloud platforms such as AWS or Google Cloud.

Support partner sales opportunities through discovery calls, product demonstrations, technical responses, solution scoping, project planning, proof-of-concept coordination, RFI/RFP responses, and security questionnaires. Collaborate with sales, professional services, product, engineering, and delivery teams while building partner relationships and developing expertise in fraud prevention and financial crime solutions.

Lead end-to-end product design for an AI-powered women’s health platform. Responsibilities include defining UX vision, creating interaction models, information architecture, wireframes, high-fidelity interfaces, prototypes, design systems, and user research. Collaborate with product, engineering, ML, marketing, and leadership teams to translate business needs into customer-focused solutions, manage design initiatives, and measure product success through user feedback and metrics.

Lead Nabla’s marketing organization and product marketing strategy across healthcare segments, buyer types, and direct and partner channels. Own positioning, messaging, launches, competitive intelligence, sales enablement, demand strategy, content prioritization, marketing operations, pipeline attribution, and team development. Manage a 6–8-person team spanning product marketing, growth content, design, and marketing operations while guiding resourcing and hiring decisions.

The Tarbell Fellowship combines three months of remote training in AI and journalism fundamentals with a nine-month newsroom placement as an artificial intelligence reporter. Fellows write and investigate AI-related topics, build journalism skills, complete coursework and assignments, and attend a weeklong in-person summit. The program is full-time and intended for candidates with journalism or AI expertise, strong writing ability, persistence, curiosity, and open-mindedness. Senior fellowships are available to candidates with at least seven years of experience and exceptional prior work.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Own the strategy, roadmap, and investment priorities for foundational agricultural data products, including pipelines, data models, APIs, and shared services. Partner with engineering, architecture, data science, and product teams to deliver scalable, governed, high-quality data capabilities. Prioritize initiatives based on customer and commercial value, define success metrics, resolve cross-team tradeoffs, and expand data support for analytics, predictive modeling, machine learning, and AI applications.

Instructor-led AI and machine learning training covering theory, data preprocessing, algorithms, model building, analytics, real-time projects, case studies, and practical business and IT applications. Participants use Python, TensorFlow, and Scikit-learn and receive certification upon successful completion.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.

Lead UX design initiatives across a healthcare product suite, solving complex user and business problems through research, prototyping, interaction and visual design. Mentor designers, influence cross-functional teams, contribute to the design system, and establish scalable design standards. Collaborate with engineering, product, leadership, and data science teams to integrate ML features and deliver intuitive, consumer-quality healthcare experiences.

Lead UX design initiatives across a healthcare product suite, solving complex problems through prototyping, user research, and strategic design. Collaborate with engineering, product, leadership, and data science teams to create consumer-quality experiences, integrate ML features, and evolve scalable design systems. Mentor designers, resolve stakeholder feedback, establish design standards, and drive innovation across large-scale projects.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Lead the engineering organization for BlueCat’s Network Observability product line. Own technical strategy, architecture, roadmap, delivery, and operational excellence for data-intensive network monitoring platforms spanning SaaS and on-premise deployments. Build AI-driven observability capabilities, modernize legacy systems, establish engineering standards, and develop distributed teams. Partner with product, executive leadership, customers, sales, and support to achieve business goals and resolve strategic technical issues.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.