Top AI & Machine Learning Jobs

Owns end-to-end delivery of enterprise fine-tuning engagements, including client discovery, use-case prioritization, ROI, scope, timelines, model hosting, SLAs, security reviews, and stakeholder management. Partners with sales, product, engineering, and machine learning teams; navigates enterprise procurement and IT processes; translates between technical buyers and internal teams; and develops repeatable processes and playbooks to scale the practice.

Develop member-level predictive models and longitudinal analyses for CMS Health Outcomes Survey measures and Medicare Advantage Star Ratings. Analyze survey drivers, trajectories, sampling, response rates, case-mix adjustments, and statistical significance. Build data pipelines and dashboards, validate methodologies, deploy models with engineering teams, and translate findings into clinical and operational interventions. Collaborate with Stars, clinical, care management, compliance, legal, and survey vendor stakeholders to ensure CMS-aligned outcomes and reporting.

Drives enterprise AI adoption by understanding business workflows, configuring no-code and low-code agents, apps, and automations, analyzing feedback, developing onboarding materials, and supporting use-case requirements. The role translates business needs into AI solutions, defines KPIs and acceptance criteria, conducts demos, manages support channels, and partners with technical product managers on rollout, adoption, and continuous improvement.

Develop and optimize machine learning methods for intrinsically disordered proteins, including generative models for protein conformational ensembles, affinity prediction, and generative chemistry. Build scalable training pipelines, adapt research methods, and collaborate with computational and experimental scientists to validate models using biophysical data.

Build and ship AI-powered requirements features, agentic workflows, and full-stack production services. Integrate and evaluate language models, develop RAG systems, prompt and model management, data pipelines, observability, evaluation harnesses, and safety guardrails. Partner with product teams and customers to prototype, validate, and iterate on AI capabilities while balancing reliability, latency, cost, and debuggability.

Leads finance transformation, data science, analytics, forecasting, anomaly detection, automation, and AI initiatives. Partners with finance, technology, and business leaders to improve reporting, financial data quality, forecasting, and decision-making. Develops data science roadmaps, deploys production models, establishes governance and responsible AI standards, measures business impact, and presents findings to executives. Coaches a data science team and manages cross-functional priorities.

Architect and build a cross-platform design product spanning mobile, desktop, web, cloud services, connected cutting machines, and hardware integrations. Lead AI-native engineering practices, LLM feature integration, architecture governance, secure and reliable connectivity, backend APIs, large-scale modernization, and safe releases. Partner across product, design, QA, firmware, manufacturing, and security while mentoring engineers and remaining approximately 90% hands-on.

Engineering Manager, Applied AI leads and coaches engineers while remaining hands-on on client engagements. Responsibilities include reviewing architecture, agent, prompt, and evaluation designs; resolving technical and client challenges; managing staffing and delivery; hiring and onboarding engineers; and improving engineering practices. The role requires strong software engineering experience, Python expertise, client-facing delivery experience, and familiarity with agents, evaluations, multi-agent systems, and AI coding tools.

Designs and leads scalable enterprise AI and machine learning solutions for clients. Responsibilities include translating business challenges into AI strategies, architecting data pipelines and model-serving systems, selecting cloud platforms and frameworks, guiding implementation teams, establishing AI governance and security standards, optimizing system and model performance, operationalizing LLMs and generative AI, and mentoring engineers.

Leads strategic technical engagements for an AI inference platform from architecture evaluation through benchmarking, model enablement, deployment, and production rollout. Builds and manages a high-performing solutions engineering team, guides customer evaluations and deployments, translates customer requirements into product priorities, and develops scalable technical go-to-market assets. Requires deep expertise in AI inference, accelerator architecture, distributed systems, serving frameworks, performance optimization, and customer-facing technical leadership.