Top Senior Machine Learning Jobs in Indianapolis, IN

Lead definition and delivery of AI-powered capabilities for a sustainability real-estate data platform. Shape roadmap, partner with customers and data science, own end-to-end features, define evaluation and trust frameworks, and ensure data foundations support AI. Drive cross-functional alignment and use AI tools in product workflow.

Lead design and implementation of real-time, event-driven data ecosystems powering personalization. Define self-service data infrastructure, governance, and architecture standards. Provide cross-team technical oversight, mentor engineers, ensure production reliability and cost optimization, participate in deployments and on-call, and collaborate on roadmaps and OKRs.

Lead credit analytics for the Send Now Pay Later product: build and own valuation and vintage models, manage credit policies, monitor portfolio health and NPL trends, integrate ML scorecards, and evaluate alternative data to optimize approvals and loss performance.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, valuation and NPV models, vintage analysis, executive reporting, model integration and testing, and alternative data integration to optimize approval rates and portfolio health.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, valuation and NPV models, vintage analysis, portfolio surveillance, executive reporting, model integration/testing, and alternative data exploration to optimize approval rates and manage portfolio risk.

Serve as the primary technical resource for channel partners to enable partner sales through training, demos, PoV engagements, joint account strategy, and creation of repeatable technical assets and reference architectures. Influence product roadmap via partner feedback and represent 7AI at events to scale partner-led pipeline and long-term customer success.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

Build full-stack marketplace features and ML Ops infrastructure for search, ranking, and renter-facing products. Develop model deployment pipelines, feature stores, real-time data pipelines, and monitoring using Chalk and Vertex AI. Collaborate with data scientists to productionize models, improve system performance and reliability, write tested code, participate in reviews, plan technical work, and communicate effectively across distributed teams.

Build full-stack marketplace features for search, ranking, and renter-facing products while supporting ML Ops infrastructure. Develop model deployment pipelines, feature stores, real-time data pipelines, monitoring, and production integrations with data scientists. Write tested code, review contributions, plan technical work, and improve system performance, reliability, and scalability. Collaborate with product and globally distributed engineering teams.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Design, implement, and scale web and data pipeline solutions focused on generative AI and LLMs. Improve AI/ML infrastructure for model development, training, deployment, and prompt engineering. Partner with product and stakeholders to prioritize applied AI use cases, ensure PHI security and regulatory compliance, mentor junior engineers, and contribute APIs and integrations to embed generative AI into healthcare applications.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

Design, build, test, deploy, and maintain production AI capabilities (agents, workflows, models, MCP servers, APIs) across a SaaS dental platform. Train and operate ML models, implement RAG and vector DB solutions, use AI-assisted tools, collaborate in design and code reviews, and support production reliability and monitoring.

Develop AI-powered solutions for personalized content and marketing. Collaborate with teams to optimize ML models and pipelines at scale, mentoring engineers and driving strategic growth initiatives.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

Own the advertising recommendation engine, including ranking quality, optimization objectives, guardrails, metrics, and model roadmap. Lead experiment design, inference standards, holdouts, rollback criteria, and production launches. Evaluate model performance and business impact with data science and stakeholders. Partner across ML, analytics, ad operations, pricing, identity, telemetry, and commercial teams. Apply advertising economics, ranking systems, SQL, and data-platform expertise to improve monetization for mobile apps and games.

Designs, builds, deploys, and operates production AI/ML and LLM systems. Responsibilities include developing RAG pipelines, fine-tuning and evaluating models, managing end-to-end MLOps lifecycles, establishing monitoring and governance practices, mentoring engineers, and leading complex cross-functional client and internal initiatives.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Leads the technical direction of an autonomous vehicle perception stack. Responsibilities include architecting scalable perception systems, developing scene detection and runtime activation, improving robustness against sensor degradation and adverse weather, integrating multimodal models, managing ML and data pipelines, monitoring field performance, leading testing and feature delivery, and mentoring engineers. Requires strong robotics, computer vision, machine learning, real-time systems, software architecture, and team leadership experience.

Lead ML technical strategy for Live Ads, designing and deploying forecasting, targeting, bidding, auction, and yield-optimization models. Build and evaluate large-scale production ML systems, create online/offline evaluation frameworks, partner with product to set objectives and trade-offs, and communicate results to stakeholders and leadership.

Lead ML initiatives to improve Sales productivity and automation: define roadmap, design and deploy production ML models and AI agents, build measurement pipelines with analytics engineers, iterate from performance and seller feedback, and act as senior technical partner across product, engineering, and Sales to drive measurable revenue impact.

Lead design and execution of a modernized, Python-native AI/ML platform for AIMS: define architecture, drive migration, build migration tooling, ensure scalability, observability, cost optimization, and reliability, prototype GenAI operational tooling, and set org-wide technical standards.