Top Machine Learning Jobs in Charleston, SC

Build and deploy machine learning models that personalize insurance products, analyze product data, design experiments, and measure model impact. The role owns the full data science lifecycle, including data wrangling, production coding, model monitoring, retraining, and experimentation. Responsibilities also include cross-functional collaboration, code reviews, mentoring data scientists, and improving platform practices.

Leads Finance of America's AI security and safeguards program, including strategy, governance, red teaming, model and application security testing, threat detection, monitoring, and control validation. Maintains the enterprise AI inventory, evaluates use cases, integrates security requirements into procurement and development processes, and partners with legal, compliance, risk, and security teams. Reports AI risk posture to executives and boards, supports audits and regulatory examinations, and builds a team while remaining hands-on.

Leads client-facing manufacturing and automotive analytics, data, AI, and digital transformation engagements. Responsibilities include executive stakeholder management, consulting, solution design, account growth, proposal development, commercial oversight, program governance, delivery leadership, and capability building. The role develops transformation roadmaps, modernizes data platforms, guides AI and analytics use cases, manages strategic portfolios up to $10M–$15M annually, and mentors multidisciplinary teams.

Manage provider data operations across onboarding, maintenance, verification, and quality improvement. Monitor offshore teams and automated solutions, develop dashboards and reporting, investigate data issues, and recommend process or tooling improvements. Partner with product, engineering, claims, and data teams to define requirements, test enhancements, quantify business impact, and improve operational scalability. The role also supports AI agents and automation while coordinating multiple initiatives in an ambiguous, metrics-driven healthcare environment.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Conduct independent AI research spanning large language models, reasoning, knowledge acquisition, factuality evaluation, and trustworthy AI. Translate research ideas into scalable prototypes, implement and extend state-of-the-art methods, design experiments, develop algorithms and evaluation methods, use AI-assisted development tools, and communicate findings through technical reports and research papers.

Lead research into novel AI architectures, learning algorithms, reasoning, knowledge representation, and evaluation methodologies. Define and execute ambitious research programs, design experiments, develop large-scale content evaluation systems, mentor research engineers, and communicate findings through technical publications. The role combines scientific leadership with hands-on innovation focused on language models, factuality, evidence quality, source reliability, and trustworthy AI systems.

Conduct advanced research spanning RF communications, digital signal processing, compressive sensing, machine learning, and resilient network systems. Develop and deploy ML pipelines for RF and time-series data using modern frameworks and MLOps practices. Lead projects from concept through demonstration, publish technical findings, and communicate results to stakeholders. Work may support 5G, non-terrestrial networks, cybersecurity, electronic warfare, SIGINT, radar, and other national security applications.

Leads enterprise field intelligence and portfolio prioritization across pharmaceutical therapeutic areas. Owns targeting, Next Best Action, field orchestration, roadmap governance, capability development, standards, and operating-model transformation. The role aligns cross-functional teams, drives scalable data- and AI-enabled solutions, improves customer engagement, and supports strategic decision-making across field teams and channels.

Conduct coding-heavy machine learning research involving model development, training pipelines, inference systems, numerical methods, performance optimization, and validation. Build reproducible workflows in Python and command-line tools, implement model and data pipeline components, debug system failures, and verify correctness and performance. The contract is fully remote, flexible, and approximately 15 hours per week, with output-based compensation.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative reasoning problems. Create 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 expertise.

Lead system safety engineering for defense-oriented autonomous ground vehicles. Perform hazard identification, risk analysis, FMEA, FTA, and related assessments; define safety architectures, requirements, concepts, and verification activities; support testing and technical reviews; apply safety standards such as MIL-STD-882E; lead safety working groups; mentor engineers; and collaborate across hardware, software, AI, robotics, and systems teams.

Lead the strategy, roadmap, and execution of an AI-driven B2B SaaS platform for government contracting. Translate RFP and customer needs into prioritized product enhancements, collaborate with AI research and cross-functional teams, apply agile methodologies using Jira, and drive successful product launches and adoption.

Lead technical pre-sales for large Oil, Gas & Energy accounts, define and implement Databricks-based data and ML/AI strategies, build technical champions, mentor teammates, and drive enterprise adoption through technical leadership and value selling.

Own G2’s research agenda for evaluating AI agents in realistic business workflows. Design benchmarks, task environments, scoring systems, automated and human grading methods, and reliability analyses. Collaborate with engineering and product teams to ship evaluation capabilities, communicate findings to customers and researchers, mentor technical teams, and establish repeatable standards. The role requires hands-on LLM and agent evaluation experience, original research, statistical judgment, product delivery, and technical leadership.

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 product strategy, roadmap execution, and measurable outcomes for an AI-enabled refinance title decisioning platform. Translate strategy into requirements, user stories, and acceptance criteria; prioritize initiatives against OKRs; and partner with engineering, data science, operations, sales, and marketing. Monitor product health, accuracy, costs, service levels, and performance through metrics and dashboards. Manage stakeholders, customer onboarding, continuous improvement, and automation opportunities across a regulated real estate workflow.

Own and scale Tilt’s credit card acquisition through affiliate partnerships, paid search, and paid social. Lead channel strategy, partner launches, targeting, experimentation, and optimization while collaborating with credit, data science, product, engineering, and marketing. Use SQL and Python to evaluate funnel performance, unit economics, approvals, activation, spend, and credit outcomes. Shape acquisition policies and application experiences to drive profitable growth.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

Designs, deploys, and maintains AI and machine learning solutions, including generative AI, LLM, RAG, and conversational AI applications. Owns agentic engineering infrastructure, application guardrails for patient-care workflows, data pipelines, analytics, model governance, monitoring, and cloud deployment. Provides architectural leadership, improves engineering throughput, ensures privacy and compliance, and translates business needs into scalable AI solutions.

Lead product positioning, go-to-market strategy, launches, customer insights, competitive intelligence, messaging, sales enablement, and thought leadership for AI-driven cybersecurity and authentication products. Translate complex AI and machine learning technologies for technical and business audiences, manage marketing plans and budgets, support customer case studies, and mentor a high-performing product marketing team. Partner closely with Product, Engineering, Sales, Marketing, and executive leadership.

Coordinates strategic AI programs across engineering, product, legal, risk, compliance, security, HR, and business teams. Develops execution plans, governance frameworks, status reporting, risk registers, and executive communications. Supports AI platform and agent deployment initiatives, AI policy development, enterprise training, stakeholder alignment, and adoption. Requires strong program management, AI technology understanding, organizational skills, and experience managing complex cross-functional initiatives.

Build and own production-grade generative AI applications for legal technology, specializing in RAG, agentic systems, advanced search, embeddings, reranking, evaluation, and LLM workflows. Responsibilities include architecting modular Python applications, refactoring legacy code, implementing testing and observability, optimizing performance and costs, ensuring resilience and security, reviewing code, and supporting deployments and incident resolution.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.