Top AI & Machine Learning Jobs

Manage development teams and application support for AI systems; oversee multiple application programming, installation, maintenance, and release projects; ensure quality, compliance, and alignment with business requirements; coach and develop staff and drive Agile delivery, process improvement, and system testing.

Design, build, and maintain AI agents, applications, and automations for finance stakeholders. Translate ambiguous business questions into decision-ready answers, shape data models and semantic views (dbt on Snowflake), prototype rapidly with AI coding tools, and partner closely with business users to deliver production-grade solutions.

Lead end-to-end strategy, roadmap, technical execution, and measurement for Answer Engine Optimization (AEO). Own retrieval, indexing, bot access, measurement systems, and productization; drive cross-functional engineering, market intelligence, and executive communications to optimize visibility and commercial outcomes with frontier LLMs and agentic search.

Build and ship end-to-end GenAI, multimodal LLM, RAG, and multi-agent systems for growth marketing. Design agentic UIs, memory and retrieval architectures, distributed training/inference, behavioral embeddings, observability/safety, and mentor teams while collaborating with product and engineering to deliver production AI products.

Lead applied ML scientists and managers to build production-grade models and optimization systems for marketing (bidding, capital allocation, ROAS). Own roadmap, OKRs, hiring, cross-functional partnerships, MLOps, experimentation, and production delivery at scale while driving measurable business impact.

The Senior Data Scientist partners with Customer Success to perform deep-dive analyses, diagnose metric shifts, build predictive models (churn/upsell), apply causal inference and experimentation, measure business impact, and develop AI/LLM solutions. Responsibilities include self-serving across the data stack for extraction, pipelining, and dashboards, defining KPIs, and delivering actionable insights to reduce churn, drive revenue, and improve operational efficiency.

Lead strategy and roadmap for AI-powered learning products, drive high-priority AI initiatives from concept to scale, partner cross-functionally to deliver responsible AI solutions, define success metrics and experimentation frameworks, and advise leadership on AI trends and opportunities.

Lead engineering teams to design and deliver an enterprise credit decisioning and credit risk platform. Drive technology strategy, ensure architecture and security compliance, enable cloud readiness and microservices-based modernization, partner with product and architects, remove delivery impediments, manage resources, and cultivate engineering talent and DevOps/Agile practices.

Lead and grow engineering teams to build a unified Credit Services platform. Drive technical strategy for credit decisioning, credit bureau and credit risk data ecosystems, ensure architecture compliance, mentor engineers, remove blockers, collaborate with product and vendors, and deliver secure, scalable microservices and data-driven solutions.

PhD-level summer intern will apply advanced analytics, AI/ML, and quantitative techniques to develop, validate, and enhance pricing, simulation, and benchmark models for capital markets. Responsibilities include model development, performance monitoring, statistical validation, reporting metrics, and collaborating with quantitative teams to deliver scalable, risk-aware solutions over a 10-week program.

Design, build, and deploy machine learning solutions in distributed systems; collaborate with engineers and subject-matter experts; evaluate and improve models; ensure data security and compliance; communicate insights to stakeholders; mentor junior data scientists.

Lead data science efforts for a defense program onsite in Hazelwood, MO. Define data requirements, build analysis systems, design/deploy ML models, collaborate with engineers and SMEs, mentor junior scientists, ensure security/compliance, and communicate results to stakeholders while improving models and establishing best practices.

Design and build distributed data systems for analysis and ML; lead and mentor data scientists; implement, deploy, and improve ML models (including deep learning and LLMs); ensure data quality, security, and compliance; integrate models into customer on‑prem and cloud environments; communicate insights to stakeholders and drive best practices.

Lead product management for enterprise GenAI and agentic AI solutions: define measurement frameworks, build analytics and dashboards, translate stakeholder needs into instrumentation and metrics, support agent operations, prototype agentic workflows, and deliver executive insights to improve agent performance and product outcomes.

The Senior Systems Data Scientist will develop methodologies for measuring trust in AV testing, manage data analysis frameworks, collaborate with teams, and provide insights for decision-making.

Design and deliver enterprise-grade AI workflow transformations on GCP. Build secure, event-driven backend services and multi-step AI agents, integrate bots with enterprise systems, implement telemetry, testing, CI/CD, HITL validation, and create reusable architecture, documentation, and runbooks while guiding adoption and governance.

Design, build, and scale AI-enabled workflow and automation solutions across collaboration platforms and enterprise systems. Translate manual processes into secure, reusable, event-driven automations using Python/JavaScript, integrate AI capabilities like Gemini, implement logging and telemetry, and partner with product, security, and business teams to deploy and sustain enterprise transformation solutions.

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

Perform data cleaning, exploratory analysis, and build/productionize ML and statistical models (including NLP) to generate business value for service and fluids-analysis domains. Collaborate with cross-functional teams, present findings to stakeholders, and work independently on analytics projects within an Agile environment.