Top Senior Machine Learning Jobs in St. Louis, MO

Own the end-to-end rebuild of Nextdoor’s consumer search experience, including query understanding, retrieval, ranking, result quality, organic and sponsored content blending, and monetization. Define search relevance metrics, partner with engineering, data science, ML, research, design, and marketing teams, and use A/B testing to improve engagement and revenue while protecting user experience.

Leads commercial, privacy, and product legal functions for a cloud technology company. Drafts and negotiates complex SaaS, partnership, reseller, and technology agreements; advises on global privacy, data protection, AI, intellectual property, and product development issues; develops contracting processes and playbooks; partners with cross-functional teams; and manages, mentors, and develops legal professionals.

Develops, calibrates, validates, and maintains credit risk models, including PD, LGD, and EAD models, for lending, collections, loss forecasting, capital planning, and stress testing. Analyzes large datasets, evaluates model performance, documents model changes, supports systems testing, and communicates statistical insights to stakeholders and management. Collaborates across the credit union to improve risk-return strategies and reporting.

Close $1M in net-new bookings by selling AI transformation to CEO-led buying committees. Generate outbound pipeline, co-sell initially with the founder, then independently close complex mid-market deals. Build a repeatable sales playbook covering ICP, discovery, multi-threading, pricing, objections, and close plans. Feed deal insights into company packaging and pricing, with a path to build and lead the future sales team.

Lead design and implementation of high-throughput, low-latency programmatic bidding systems. Collaborate with ML engineers to deploy real-time inference, optimize distributed microservices, run experiments and quantitative analysis, and partner with product to ship scalable features. Foster operational excellence, mentor engineers, and continuously improve performance and reliability.

Own the product strategy, roadmap, team, customer relationships, and P&L for Teamworks’ Coaching, Player Personnel, Recruiting, and Intelligence portfolio. Lead AI strategy across sports markets, integrate acquired products, align engineering, design, data science, marketing, and sales, and convert proprietary sports data into AI-powered workflows. Represent the business with teams, leagues, and partners while driving multi-market growth and product execution.

Leads technical sales and executive evangelism for Verint’s Open CCaaS and CX Automation platform. Acts as a trusted advisor to C-suite customers, articulating ROI, AI, data science, and platform capabilities. Partners with Product, Sales, Solutions, Services, Marketing, and Customer Success teams to drive adoption, expansion, thought leadership, and opportunity closure. Requires deep contact center operations experience, strong data architecture knowledge, executive communication skills, and extensive travel.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Lead product analytics to improve user growth, engagement, retention, and revenue. Design and analyze A/B and multivariate experiments, perform funnel/cohort analyses, maintain KPIs and dashboards, ensure event tracking, leverage ML and AI tools for advanced analyses, and translate insights into product recommendations across cross-functional teams.

Lead design and scaling of a unified fraud and risk platform. Define product strategy, own fraud metrics, run SQL-driven analysis and A/B tests, and partner with engineers and data teams to build real-time detection, identity verification, and transaction monitoring. Balance customer experience with risk mitigation and communicate impact to executives, legal, and partners.

Design and lead end-to-end AI solution architectures, ensuring scalability, security, and compliance. Translate business and regulatory requirements into technical designs, evaluate tools/platforms, document architecture standards, coordinate technical teams, and support deployment and ongoing improvements.

Lead complex, cross-functional AI programs end-to-end: translate strategy into integrated plans, drive multi-team execution, mitigate risks (data, model, compliance), apply ML lifecycle practices, use Agile/kanban methods, manage budgets/vendors, communicate to senior stakeholders, and mentor other program managers to improve AI program standards.

Lead technical pre-sales for large Oil, Gas & Energy accounts, define and implement Databricks-based data and ML/AI strategies, build technical champions, mentor teammates, and drive enterprise adoption through technical leadership and value selling.

Drive go-to-market execution across Nabla’s product portfolio, including positioning, messaging, sales enablement, competitive intelligence, demand-generation partnerships, customer adoption content, and event support. Progress into owning go-to-market strategy for specific products from early definition through launch and adoption, partnering with Product and Engineering and tracking launch success metrics.

Establishes and maintains enterprise data governance and stewardship frameworks, policies, controls, ownership models, quality standards, lineage, catalogs, metadata, and lifecycle practices. Partners with Security, Legal, Compliance, Technology, analytics, AI, and business stakeholders to support privacy, responsible data use, regulatory adherence, and client commitments. Monitors governance adoption, identifies risks and quality issues, leads remediation, develops reporting and training, and serves as a cross-functional data governance subject matter expert.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead end-to-end healthcare analytics projects, managing scope, planning, execution, client relationships, cross-functional collaboration, risks, and delivery. Provide thought leadership in healthcare analytics and identify business opportunities within client accounts. Required capabilities include quantitative analysis, project management, communication, presentation, SQL/Python/R proficiency, and strategic use of AI tools. Preferred experience includes consulting, cloud analytics platforms, healthcare data regulations, predictive modeling, machine learning, and data visualization.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.