Top Senior Machine Learning Jobs in Indianapolis, IN

Design, build and deploy production AI/ML solutions from complex, distributed farm data. Engineer features, select models, run experiments, move models to scalable real-time production, monitor and observe AI systems, evaluate emerging AI technologies, and collaborate with product, engineering and domain teams. Contribute to user-facing features with React/TypeScript as needed.

Design, build, and deploy production-grade AI/ML solutions that convert complex, distributed farm data into actionable intelligence. Engineer features, select models, run experiments, and move models to scalable, real-time production. Integrate, fine-tune, monitor, and observe models on cloud infrastructure (AWS, Databricks). Partner with product, engineering and domain teams and contribute to user-facing experiences when needed.

Lead design, development, and production deployment of ML and generative AI solutions at scale. Drive personalization, pricing, experimentation, agentic AI, and simulation modeling. Partner with engineering and product teams, mentor engineers, influence technical strategy, and measure business impact through data-driven experimentation.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Build and maintain backend and full-stack marketplace features while supporting ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring). Collaborate with data scientists to bring models to production, write well-tested code, address performance and scalability issues, and contribute to technical planning and cross-functional communication.

Build and maintain full-stack marketplace features and support ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring) on Chalk and Vertex AI. Collaborate with data scientists to productionize models, write well-tested code, address performance/scalability issues, and contribute to technical planning.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

Design, build, and deploy scalable distributed deep learning pipelines and infrastructure. Optimize hardware utilization, profile and benchmark training, manage large datasets, and enable reproducible ML workflows in cloud environments. Collaborate with ML scientists and engineers to align pipelines with research goals and drive performance, reliability, and operational best practices for large-scale model development.

The role involves leading ML strategy, architecting systems, designing algorithms, collaborating with teams, and mentoring engineers in AI for enterprise analytics.

The role involves applying AI/ML techniques to develop model systems for healthcare, collaborating across teams, and conducting R&D.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

Own end-to-end ML lifecycle for pricing and underwriting: improve model accuracy and coverage, reduce infrastructure cost, deploy and monitor production models on AWS, run experiments/backtests, and collaborate with domain experts to inform model improvements and product APIs.

The Lead Machine Learning Engineer will design and evaluate models for autonomous driving systems, mentor junior engineers, and utilize large datasets to improve ML capabilities at May Mobility.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

Design, fine-tune, evaluate, and deploy LLM-driven, user-facing browser features using RAG, embeddings, prompt engineering, and modern ML tooling. Lead end-to-end model lifecycle, run production experiments, collaborate with product and engineering, and document and review work with an emphasis on privacy, latency, and usability.

Lead MLOps for production ML lifecycle: deploy and serve models, build scalable reproducible pipelines for large protein-design workloads, implement CI/CD and observability, optimize GPU/accelerated compute, and enable self-service tooling for researchers.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Lead client-facing architecture and delivery of production-grade GenAI systems. Design scalable LLM, agentic, RAG, recommender, and LTR solutions, manage client engagements, mentor ML engineering teams, and oversee project lifecycle from discovery to deployment and monitoring.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

The Senior Manager, Data Science/ML will lead a team to enhance product innovation through data science, focusing on ML and experimentation frameworks that boost customer engagement. Responsibilities include defining product strategy, building ML models, and collaborating with cross-functional teams to shape product roadmaps.

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

As a Senior/Staff ML Systems Engineer at Quilter, you'll design and maintain ML infrastructure for automated PCB component placement, manage data pipelines, and lead systems design reviews while ensuring project efficiency and reliability within a fully distributed team.