Top AI & Machine Learning Jobs

Full-time, 12-week data science internship contributing to business analytics projects. The intern will use SQL, Python, Power BI, and cloud analytics tools to build dashboards and analytical models, investigate data, collaborate with analytics teams and business leaders, and present project outcomes to senior leadership. The role also includes mentorship, professional development, networking, and exposure to organizational operations.

Optimize training and inference performance for structural and generative ML models. Develop custom CUDA and Triton kernels, use ML compilers, scale distributed training across multi-node GPU clusters, reduce inference costs, improve GPU utilization on GCP, and build benchmarking and profiling tools. Provide technical direction, select infrastructure, influence research teams, and mentor engineers.

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.

Participate in a 12-week data science internship by analyzing business problems, building dashboards and analytical models, using SQL, Python, and Power BI, collaborating with analytics teams, and presenting project outcomes to senior leadership. Interns will also receive mentorship, professional development, and exposure to organizational operations.