Top AI & Machine Learning Jobs

Designs, develops, and deploys agentic AI solutions and process automation for Samsung care operations. Responsibilities include multi-agent workflows, MCP servers, enterprise API integrations, databases and vector stores, low-code automation, cloud environments, CI/CD, monitoring, process analysis, and AI governance. The role leads use cases from proof of concept through production, collaborates with business stakeholders and Korea HQ, and communicates technical implementations to nontechnical audiences.

Lead data science measurement initiatives for media and advertising. Design experiments and causal models to measure incrementality, build marketing mix models, and productionize scalable data pipelines. Develop and deploy models using Python, R, SQL, and cloud platforms while ensuring data quality and privacy compliance. Deliver dashboards, automated reporting, and executive-ready insights, communicate findings to stakeholders, and mentor junior data scientists.

Own AI projects from discovery through production, building and deploying AI-powered applications and workflows for GTM, operations, product, strategy, and partner teams. Translate ambiguous processes into scalable systems, integrate applications with the GTM technology stack, gather user requirements, prioritize projects, and coordinate cross-functional execution. Use structured data and context to improve AI reliability and track business performance.

Supports the development and deployment of production AI systems by fine-tuning and evaluating machine learning models, integrating AI features, working with training and evaluation data, running experiments, monitoring performance, and collaborating with engineering and product teams. Requires current enrollment in a relevant bachelor's or master's program, Python proficiency, familiarity with PyTorch or TensorFlow, and foundational machine learning knowledge.

Own the enterprise AI technical strategy, including model, vendor, platform, architecture, cost, security, and reliability evaluations. Establish AI standards, reference architectures, governance processes, and usage and spend visibility. Advise executives and engineering teams, assess AI risks, and partner with Risk, Compliance, and Legal. Prototype and validate LLM, API, agent, and RAG capabilities through working code, scaling productive experiments into reliable organizational capabilities.

Own AI product areas from discovery through launch, defining problems, writing PRDs, building prototypes, conducting customer research, partnering with engineering and design, and establishing success metrics. The role requires translating complex generative AI capabilities into useful enterprise workflow experiences, accounting for probabilistic behavior and model failure modes, and shipping technically complex products with measurable outcomes.

Hands-on machine vision applications engineer responsible for lab testing, camera and optical setup, proof-of-concept builds, technical reporting, technology evaluation, customer-site troubleshooting, and deployment support. The role also includes image collection, labeling, model training, model evaluation, camera calibration, hardware evaluation, and building internal test resources. Candidates need machine vision fundamentals, AI vision familiarity, troubleshooting ability, and comfort working with hardware, industrial automation, and manufacturing customers.

Build and ship AI tools from prototype to adoption across partner communities. Own technical architecture, build-versus-partner decisions, privacy-safe deployments, testing, bias control, governance, and scale strategy. Manage technology and funding partnerships, pursue AI program opportunities, develop go-to-market budgets, and present recommendations to executive and board audiences. Translate tools into practitioner documentation and training while ensuring human oversight and data dignity.

Leads development of agent evaluation systems that assess live software agents using trace data, rubrics, benchmarks, and LLM-as-a-judge techniques. Owns the evaluation platform’s full stack, creates reusable workflows and integrations, tracks emerging evaluation methods, and collaborates with data science teams. Manages and mentors engineers while promoting evaluation practices across agent-focused products.

Own end-to-end data science projects, including customer discovery, statistical analysis, predictive modeling, Python and SQL development, Databricks pipelines, AI-enabled analytics, quality checks, and customer presentations. Build reusable data assets at claims-data scale and contribute to Bonfire’s AI-powered products. The role requires rigorous analysis, strong communication, customer empathy, and comfort working with ambiguous problems.