Top AI & Machine Learning Jobs

Build and operate machine learning models powering roleplay, scoring, and coaching products. Own the full lifecycle from training and fine-tuning through production deployment and maintenance. Fine-tune open-source models, optimize them for cost and latency, deploy them on-device when needed, and develop evaluation frameworks, benchmarks, and regression suites. Collaborate closely with founders and engineers to shape product direction.

Educates employees and business leaders on practical AI use, especially Microsoft Copilot and approved AI tools. Delivers workshops, demonstrations, coaching, and reusable learning materials; identifies AI and automation opportunities; documents use cases; supports change adoption; and tracks usage, time savings, quality improvements, feedback, and business value.

Conduct research advancing healthcare AI, including model evaluation, clinical benchmarks, human assessments, health-focused model development, agentic systems, prototyping, and simulations. The role involves pre-training and post-training workflows, research publications, collaboration with clinical and engineering teams, and sharing research practices. Candidates will apply expertise in machine learning, natural language processing, clinical data, and healthcare data standards such as FHIR and OMOP.

Productionize detection, tracking, and vision-language models across edge devices and cloud infrastructure. Build evaluation systems, data feedback loops, deployment pipelines, and production metrics; optimize inference cost and performance; run staged rollouts and experiments; and ship customer-facing features connecting inference services, APIs, and product behavior.

Lead technical direction for AI infrastructure, data platforms, shared software primitives, and agentic engineering practices. Build scalable, cloud-native services and secure MCP integrations while establishing AI quality gates, evaluation frameworks, responsible-AI controls, SLOs, and architectural standards. Guide modernization, build-versus-buy decisions, and high-stakes platform choices. Mentor senior engineers and influence technical norms across teams while balancing product delivery, partner needs, member trust, security, and operational quality.

Build and deploy machine learning models and end-to-end pipelines for marketing targeting, customer outcomes, and loan origination. Responsibilities include feature engineering, experimentation, model evaluation, monitoring, documentation, governance, and translating analytical findings into business recommendations. The role collaborates with marketing, analytics, product, and cross-functional stakeholders to deliver explainable, compliant solutions.

Build and maintain scalable MLOps infrastructure supporting the full machine learning lifecycle. Responsibilities include developing Databricks and Spark pipelines, operationalizing and monitoring models, managing CI/CD, streaming data, versioning, governance, automated retraining, distributed workload optimization, and low-latency model serving. The role collaborates with data scientists and platform teams to deliver reliable batch and real-time ML systems, including recommendation, personalization, and LLM-based products.

Leads architecture, development, testing, and delivery for scalable enterprise software platforms. Drives adoption of AI-assisted development, intelligent automation, AI agents, copilots, observability, anomaly detection, and workflow solutions. Establishes engineering, quality, security, scalability, governance, and responsible AI practices while partnering with engineers, business stakeholders, and senior leadership.

Leads advanced analytics initiatives supporting strategic planning, equity tracking, forecasting, budgeting, staffing, and resource allocation. Builds predictive models, simulations, and decision-support tools using education and enterprise data. Presents insights to executive and board-level audiences, partners with academic, operational, and finance teams, maintains reproducible workflows and data governance compliance, collaborates on data quality, and mentors analysts.

Own enterprise data inventory and translate business needs into data models, quality rules, mappings, and acceptance criteria. Partner with data and software teams on pipelines, governance, monitoring, and lifecycle management. Prepare data for agentic workflows, including semantic layers, knowledge bases, retrieval indexes, and evaluation datasets. Develop reusable analytical models, reports, dashboards, and decision-support products while monitoring data quality and communicating limitations and business impact.