Top AI & Machine Learning Jobs

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Lead data science initiatives supporting Product and Commercial Operations. Analyze growth opportunities, define success metrics, conduct experimentation and causal inference, and deliver actionable insights. Lead initiatives such as experimentation systems and data agents while promoting strong analytical practices. Partner with stakeholders to drive measurable product impact, minimize marketplace losses, and communicate complex findings to executive audiences.

Architects and leads production AI products, including chatbots, document processing, agentic workflows, and centralized AI platforms. Designs model routing, vector search, retrieval, context engineering, MCP integrations, and scalable cloud-native systems. Integrates AI with accounting systems, repositories, APIs, and enterprise technologies while ensuring monitoring, compliance, and observability. Provides technical leadership, establishes AI development standards, and mentors engineers.

Build AI-native program-analysis systems that combine static/dynamic analysis and LLMs to detect and fix bugs, validate fixes against CI, index and retrieve code context, orchestrate review agents, and own production qualities like correctness, latency, cost, and security while working with customers and SonarQube integrations.

Designs, builds, and operationalizes production data science, machine learning, generative AI, and LLM systems. Responsibilities include data and AI pipelines, predictive models, RAG applications, experimentation, MLOps, model serving, monitoring, governance, cloud migration, and business analytics. The role partners with Product, Engineering, BI, and Data teams to translate business problems into scalable AI solutions and measurable product or operational improvements.

Build predictive lending models, analyze consumer credit risk and portfolio trends, establish performance metrics, create automated SQL and Python dashboards, and design A/B tests for underwriting strategies. The internship runs May through August 2027 in a hybrid Chicago office setting and requires full-time availability.

Lead Braze’s Applied AI Architects across GTM and corporate functions. Build and develop a senior technical team, manage the engagement portfolio, prioritize and measure AI initiatives, maintain hands-on involvement in agent design and delivery, and partner with product, technology, and executive leaders to scale successful solutions. Define the operating model and career framework for this emerging role category while driving measurable business outcomes such as sales ramp time, quota capacity, and competitive win rate.

Leads and grows a perception AI engineering team building autonomous spacecraft capabilities, including synthetic data generation, multi-target tracking, sensor fusion, camera calibration, object detection, and edge deployment. Provides technical direction, defines validation standards, owns the team roadmap, coordinates cross-functional delivery, manages hiring and development, and ensures reliable performance under spacecraft compute, power, and operational constraints.

Develop and deploy machine learning models and data pipelines for automated decision-making. Responsibilities include collecting and transforming data, exploratory analysis, predictive modeling, production deployment, model serving infrastructure, monitoring drift and performance, and communicating insights to stakeholders. The role requires strong Python and SQL skills, experience with cloud platforms and MLOps tooling, and collaboration across global teams.

Build and maintain knowledge and evaluation infrastructure for long-horizon AI agents. Develop production agents for complex customer use cases, analyze agent errors, and implement quality improvements. Build reliable backend systems supporting agent execution, evaluation, and monitoring, while partnering with senior engineers on platform reliability and quality. The role may also involve platform engineering, frontend work, and distributed systems.