Top AI & Machine Learning Jobs

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.

Supports ADAS environmental modeling teams by developing, integrating, testing, tuning, and debugging perception algorithms for parking, trailering, surround view, camera calibration, and sensor fusion. Responsibilities include image processing, deep learning model development, test-case creation, system testing in bench and vehicle environments, troubleshooting, root-cause analysis, issue documentation, and proposing corrections.

Build and ship production algorithmic systems for healthcare, including machine learning, optimization, heuristics, and expert systems. Responsibilities include framing complex problems, defining objectives and metrics, validating solutions, deploying models, iterating based on outcomes, reviewing code and analyses, and communicating findings to stakeholders. Example projects include provider tiering optimization and member engagement models using claims and behavioral data.

Lead marketing measurement and experimentation to quantify attribution and incrementality, optimize ad platforms and CRM, build performance data assets, analyze lifecycle engagement, and communicate insights to influence marketing strategy and spend allocation.

Develop and deploy machine learning systems for humanoid robot manipulation, covering data collection, annotation, synthetic data generation, model training, simulation, hardware deployment, and fine-tuning. Diagnose vision, calibration, sensor fusion, model, and data bottlenecks to improve reliability. Build real-time robotic software using modern computer vision and deep learning, while mentoring engineers and driving architectural excellence across the robotics organization.

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines using Python, SQL, machine learning, and large transactional datasets. Partner with Fraud Operations to refine models, reduce false positives and negatives, analyze emerging fraud trends, and improve underwriting and verification strategies. Communicate insights to senior stakeholders, apply AI in production, and lead team members and initiatives.

Build and deploy AI-augmented workflows and agents to automate internal processes across departments. Identify high-value automation opportunities, optimize operational workflows, create reusable documentation and enablement resources, measure business impact, and help establish safe AI governance standards. The role requires cross-functional consulting, stakeholder management, process analysis, and practical experience with low-code platforms, LLM APIs, prompt engineering, or AI agent frameworks.