Top AI & Machine Learning Jobs

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.

Design, build, deploy, and operate secure, scalable AI-enabled products and internal tools. Evaluate models and services, develop prompts and context strategies, establish evaluations, monitoring, safeguards, and reusable architectures, and guide responsible AI adoption. Partner with product and engineering teams to prioritize opportunities, balance value and risk, mentor colleagues, and improve AI systems using production data and stakeholder feedback.

Develop and support AI-powered applications, automation solutions, and agent-based systems for business operations. Responsibilities include software development, integrations, Azure AI configuration, RAG and prompt engineering, testing, architecture participation, monitoring, troubleshooting, documentation, security collaboration, code reviews, and user enablement. The role works cross-functionally with engineers, stakeholders, legal teams, data professionals, and technology partners to deliver scalable and reliable AI solutions.

Design, build, evaluate, and deploy scalable AI models for insurance business problems. Analyze large datasets, validate models, monitor performance, and collaborate with data scientists and engineers to align solutions with business objectives and communicate results to non-technical stakeholders.

Leads the design, development, deployment, and validation of advanced analytics and machine learning solutions. Partners with clients and stakeholders to translate business objectives into analytical strategies, analyzes complex datasets, communicates insights, mentors technical teams, establishes model governance practices, and supports project planning, solution architecture, delivery, and business development.

Data Scientist Intern will collaborate with business teams in an agile environment to develop insurance risk, claims, retention, anomaly detection, and process-efficiency models. The role includes using internal and third-party data, generalized linear models, machine learning, AI techniques, and data visualization. Candidates should be pursuing a relevant degree, maintain a minimum 3.0 GPA, and demonstrate quantitative, programming, communication, analytical, and teamwork skills. This is an in-office internship in West Bend or Madison, Wisconsin.