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.

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.

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.

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.

Own the architecture and technical direction of agentic AI platforms and intelligent applications across web, mobile, backend, conversational, and voice interfaces. Define agent orchestration, tool integration, retrieval, memory, evaluation, observability, security, and governance standards. Lead client-facing solution architecture, establish reusable frameworks and reference architectures, and guide teams on model selection and reliability, cost, latency, and security tradeoffs. Prototype complex solutions while shaping organization-wide technical direction.

Build and operate Retool’s production agent platform across product, infrastructure, and evaluation layers. Design agent prompting, context construction, retrieval, routing, tool use, sandboxing, and evaluation systems. Diagnose nondeterministic failures, improve reliability and safety, and partner on workload provisioning and deployment across cloud and self-hosted environments. Mentor engineers and establish standards for dependable agentic products.

Supports scalable ETL/ELT pipelines, cloud data platforms, database integration, machine learning model development and deployment, AI-enabled workflows, and analytics dashboards. Uses Python, data science libraries, AWS, GCP, Azure, SQL and NoSQL databases, Looker, Tableau, and automation tools. Collaborates with data engineers, analysts, data scientists, and marketing teams to deliver data-driven solutions. The role is hybrid in Dallas, requiring two in-office days weekly.