Top Senior Machine Learning Jobs in Des Moines, IA

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Designs, develops, tests, implements, maintains, and supports custom software applications across internet security, content delivery, APIs, machine learning, and cloud computing. The role improves platform functionality, develops new capabilities, automates product operations, contributes to architecture and software design, and expands automated unit, functional, integration, and end-to-end testing.

Serve as the persistent technical partner for strategic and premium customers, diagnosing workflows, labeling, configurations, model behavior, and technical health. Build success plans with Customer Success Managers, guide implementations and model releases, conduct technical health reviews, coordinate fraud incident responses, and communicate technical findings to business and engineering stakeholders. Identify recurring customer patterns and provide feedback to Product and Engineering while supporting account retention and growth.

Own technical relationships with insurance carrier accounts by leading discovery, solution design, integrations, deployment support, defect management, QA, documentation, API and payload validation, SQL-based account monitoring, and incident management. The role partners with client engagement and engineering teams to ensure deployments meet carrier requirements and progressively assumes greater ownership of technical account relationships.

Design and deliver secure, high-performance microservices and APIs. Develop backend services in Java and Python, integrate ML components, build front-end service layers, deploy containerized applications to cloud platforms, and collaborate in Agile teams to ensure scalable, test-driven solutions.

Owns the backlog, sprint cadence, releases, and production health of enterprise AI products. Translates product strategy into requirements, user stories, work packages, and sprint goals while coordinating engineers, data scientists, business partners, and dependent teams. Leads Scrum ceremonies, PI planning, testing, release communications, and uptime monitoring. Applies AI tooling to improve delivery and uses technical fluency to prototype, review pull requests, and support responsible AI implementation.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Leads strategy, roadmap, execution, commercialization, and customer outcomes for a major enterprise software product area. Conducts market and customer discovery, prioritizes opportunities, partners with engineering and design, establishes outcome-based metrics, and supports sales enablement. Builds and coaches the product management team, manages executive and customer relationships, and helps transition the company from services-led to product-led operations. The role emphasizes legal technology, eDiscovery, AI-enabled workflows, enterprise data, and regulated-industry requirements.

Lead architecture and technical strategy for a remote engineering organization building a React, Node.js, PostgreSQL, and AWS platform. Mentor engineers, establish engineering standards, drive JavaScript-to-TypeScript migration, design ETL and AI/LLM integration systems, resolve technical debt, and oversee technical design reviews, production support, incident response, hiring, and delivery. Partner with product, clinical, and business stakeholders to build scalable, reliable software.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Own technical delivery and account health for insurer software deployments. Responsibilities include discovery, solution design, integrations, API and payload review, QA testing, defect documentation, workflow mapping, SQL-based production monitoring, incident management, and technical communication with carrier accounts and engineering teams.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Build and scale accounting software synchronization solutions connecting BILL’s payment network with third-party accounting platforms. Lead end-to-end software design and delivery, develop scalable AWS microservices, improve platform performance, guide testing and CI/CD practices, resolve customer problems, mentor engineers, and use generative AI to accelerate development.

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Leads the full lifecycle of clinical NGS diagnostic algorithms for oncology and liquid biopsy applications. Responsibilities include algorithm design, analytical validation, design control, regulatory readiness, variant calling, QC metric development, probabilistic modeling, machine learning, and validation pipeline automation. The role serves as technical authority for algorithm changes, supports regulatory submissions, and develops solutions for low-input, fragmented, and ultra-low-frequency genomic variants.

Serves as a permitting and plan review domain expert for engineering, product, design, data, and go-to-market teams. Reviews permit applications, plan sets, submittals, and AI outputs; interprets building codes; identifies missing or noncompliant information; defines approval and revision criteria; labels examples for ML systems; synthesizes jurisdictional requirements; and develops rubrics, checklists, documentation, and benchmarks. Requires approximately 5+ years of hands-on permitting or plan review experience and fluency in applicable building codes.

Leads the Product Acquisition engineering team and drives technology strategy, architecture, software delivery, DevOps maturity, platform adoption, vendor management, and operational excellence. Partners with product managers on roadmaps and business outcomes, ensures engineering standards and production support, and coaches, recruits, and develops engineers and consultants. Requires strong software development, product operating model, project management, stakeholder communication, and technical leadership experience.

Own the vision, strategy, roadmap, business case, licensing, lifecycle policy, deployment models, and go-to-market strategy for GridBeats Device Management. Lead customer discovery, product requirements, release planning, cross-functional delivery, sales enablement, and KPI tracking. Partner with utilities, engineering, commercial, marketing, and global sales teams to deliver secure device-management software for grid operators and industrial customers.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.