Top Machine Learning Jobs in Indianapolis, IN

The Research & Development Clinical Pathologist will collaborate on innovative solutions in veterinary medicine, leveraging technology and clinical pathology expertise.

Lead the advancement of embedding-based retrieval systems, focusing on multimodal models, model performance, and extensibility for various use cases.

Lead the strategic direction of data science at Mission Lane, innovating machine learning applications and managing a high-performing team to enhance financial outcomes.

The VP of Client Development will lead InMobi's growth strategy within the OEM and Telco sectors, establishing executive partnerships, joint business plans, and aligning product innovation to maximize market opportunities. The role requires extensive experience in enterprise partnerships and strategic business development with global technology leaders.

NextGen offers an internship for students to learn data science and machine learning. Interns will receive mentorship and work on real projects, gaining skills in Python, ML algorithms, data analysis, and visualization.

Develop and maintain scalable AI/ML architectures, collaborate on integrating solutions into products, and ensure compliance with industry standards.

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.

Lead technical direction and execution of Lime's CX automation platform, building scalable backend services, decision engines, and human-in-the-loop workflows to automate rider issue resolution while enforcing fraud and trust guardrails and improving operational metrics.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Design and deliver scalable AI solutions powered by Large Language Models. Collaborate with teams to create cloud-native architectures, enable knowledge discovery, and mentor engineers in AI initiatives.

This role involves defining and managing the AI platform's roadmap, collaborating with cross-functional teams to integrate AI capabilities, and ensuring product quality and reliability metrics.

Design, build, and maintain scalable AI/ML architectures; collaborate with data scientists, engineers, product, and compliance to integrate models; evaluate tools and processes; monitor performance and ensure industry-standard compliance and ethical practices.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

Design, build, and operate production-grade ML systems for personalization, search, and recommendations. Own end-to-end pipelines from data ingestion and modeling to deployment, monitoring, and iteration, ensuring scalable, reliable, and observable services.

The Senior AI/ML Engineer will design, build, and deploy Generative AI and ML platforms, ensuring compliance and high performance. Responsibilities include developing foundational services, overseeing model training infrastructure, and mentoring junior engineers.

Lead the ML and AI engineering team, strategizing and implementing AI solutions for business growth, mentoring staff, and collaborating across departments.

Design and implement AI-driven features for claims synthesis, leveraging LLMs and statistical techniques, while ensuring system performance and accuracy.

As a Senior Machine Learning Platform Engineer, you'll architect a scalable ML platform, collaborating with teams to ensure compliance, optimize performance, and guide best practices in ML workflows.

Build and own ML platform infrastructure and tooling to enable model development, deployment, monitoring, and iteration. Lead realtime model monitoring, experiment tracking, reproducibility, and operational reliability to scale fraud-prevention ML systems in production.

Manage AI/ML projects from Proof of Concept to Minimum Viable Product using Agile methodologies while ensuring stakeholder communication and project alignment.

The role involves building and maintaining ML pipelines, optimizing workflows, and contributing to product strategy with a focus on usability.

Design, build, and maintain ELT pipelines and analytics-ready data models to support reporting, KPIs, and downstream ML use cases; implement data quality, optimize pipeline performance, document lineage, and ensure governance and privacy.

Design and maintain datasets, build and evaluate domain-specific ML and vision-language models for real-time privileged access threat detection; deploy and optimize Python/Docker inference services integrated with WebSocket/WebRTC and protocol-level interfaces; monitor and document production models.

Lead QA for AI/ML features: design and execute test plans, build Python automation to validate LLM outputs, guardrails, and data pipelines; perform regression, security/privacy validation, and analyze logs/metrics to ensure reliability.

The Senior ML Engineer will develop and optimize machine learning solutions for audience intelligence, build production pipelines, and collaborate with cross-functional teams to enhance user engagement and revenue.