Top Senior Machine Learning Jobs in Greensboro, NC

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Build and scale core components of an end-to-end proteomics software stack (LIMS, data pipelines, web portal). Design agentic backends and AI-augmented tooling, develop analysis algorithms and lab integrations, deploy cloud IaC and CI/CD, write tests and monitoring, and collaborate with R&D, operations, and customers to productionize tools for high-throughput proteomics.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

The AI Sales Executive drives new business growth and expands client relationships by selling AI-driven solutions for engineering and manufacturing sectors, managing the full sales cycle with a consultative approach.

Lead program delivery for engineering teams at Arine, optimizing internal processes and ensuring timely execution of technical programs while facilitating Agile methodologies.

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

As a Regional Sales Director, you'll lead a sales team, develop business plans, and drive sales growth by managing complex cycles and nurturing talent.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Own Oscilar's AML product suite (transaction monitoring, KYC/KYB, screening, case management, SAR filing, AI agents). Define detection rules, model calibration, data strategy, and defensible audit trails. Collaborate with engineers and compliance, use AI across discovery/design/analysis, deliver PRDs, validate models, and ship production features.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

Own and implement end-to-end product features across the stack (React/TypeScript front end, Ruby on Rails backend, relational DBs, GCP infra). Design APIs and data models, ensure performance and reliability, integrate AI/LLM-powered features, collaborate with product/design/clinical teams, perform code reviews, and mentor junior engineers.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Develop and iterate deep learning models for protein-ligand co-folding and structure prediction, benchmark performance, integrate validated models into production drug-discovery pipelines, analyze structural and sequence data, communicate findings, and collaborate with multidisciplinary teams to scale research into products.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

Design, build, and deploy production-grade AI/ML and LLM-based systems. Lead end-to-end ML lifecycle, implement RAG and embedding pipelines, establish MLOps best practices, mentor engineers, and collaborate with cross-functional teams to deliver scalable, responsible AI solutions.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

The role involves improving AI agent quality through designing experiments, maintaining evaluation infrastructure, optimizing LLM inference, and collaborating with product teams. Responsibilities include end-to-end ownership from research to deployment in a dedicated ML Engineer context.

The Lead Machine Learning Scientist will develop and implement ML strategies to enhance marketing and product capabilities, identify opportunities for growth using data, lead core model development, and improve marketing spend efficiency through insights and experimentation.

Design and operate production ML systems for large-scale streaming data, ensuring reliability and performance while collaborating with researchers to integrate models into distributed systems.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.