Top Senior Machine Learning Jobs

The Senior Staff Machine Learning Engineer will develop and scale ML systems for content classification, moderation, and decision-making, ensuring quality and compliance across Spotify's platform.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Provide advanced discovery and analytical support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, document methods and collaborate with analysts and engineers to support mission-focused discovery efforts and adopt new analytical technologies.

Designs, implements, and deploys scalable AI/ML solutions: selects algorithms, engineers features, trains and tunes models, integrates models into production, monitors performance, mentors team members, leads design reviews, and communicates technical strategy to stakeholders.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Lead development of large-scale reinforcement learning frameworks and simulation pipelines for autonomous vehicle foundation models. Design closed-loop simulation, optimize reward functions and simulation quality, build scalable data-processing workflows, and improve training reliability on GPU clusters. Develop monitoring and debugging tools while collaborating with platform, product, and research teams to integrate advanced model architectures and productize machine learning solutions.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Lead the design and implementation of advanced ML models for autonomous driving, collaborating with teams, mentoring engineers, and driving technical initiatives.

Lead development of multi-sensor, multi-task machine learning models for autonomous driving perception, including object detection, occupancy prediction, road understanding, and trajectory planning. Architect foundational modeling libraries, sensor fusion components, temporal aggregation systems, and scalable experimentation pipelines. Optimize models for latency, throughput, memory, and onboard hardware constraints while enabling rapid prototyping and analysis of model training and learned representations.

Own end-to-end computer vision and machine learning projects for power grid infrastructure, from problem framing and research experimentation through production deployment and monitoring. Develop detection, segmentation, classification, anomaly detection, and foundation-model solutions; build data pipelines, serving systems, experiment tracking, and model versioning. Conduct error analysis, benchmarking, tuning, code reviews, and testing while translating client requirements into reliable production models and communicating technical decisions and limitations.

Develop and optimize machine learning models for defense and intelligence applications, including computer vision, time-series analysis, embeddings-based change detection, and satellite imagery analytics. Build scalable preprocessing and inference pipelines, integrate models with software platforms, and establish testing, validation, monitoring, and reliability frameworks. Collaborate with product managers, data scientists, and engineers to deploy high-throughput solutions globally.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

Perform data and computer science analysis to support customer operations using advanced machine learning, statistical methods, and programming (Python, Java, C/C++). Curate and process large, messy national agency datasets, build models and inferences, translate mission requirements into technical solutions, and communicate results across teams. TS/SCI with polygraph clearance required.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

As a Senior ML Engineer, you'll design, develop, and deploy ML models for product features, collaborate with teams, and drive research initiatives.

Lead and grow a team of ML researchers and engineers to define strategy and roadmap for next-generation recommendation systems. Translate cutting-edge research into production systems, collaborate cross-functionally, mentor talent, and drive metrics-backed product outcomes using state-of-the-art ML and generative techniques.

Design, develop, and deploy enterprise-scale AI systems powering intelligent agents, copilots, and autonomous workflows. Build production-grade LLM, RAG, vector database, and agent orchestration solutions; develop APIs, SDKs, and platform services; and optimize reliability, latency, security, cost, and scalability. Lead initiatives through production, implement testing, CI/CD, monitoring, and governance, troubleshoot distributed systems, collaborate cross-functionally, contribute to architecture and documentation, and mentor or guide team members.

Lead and grow an ML/AI operations engineering team responsible for reliable, scalable production systems. Own training pipelines, feature stores, model serving, monitoring, CI/CD, observability, incident response, and generative AI operations. Partner with data science, product, engineering, legal, and privacy stakeholders to establish operational standards, manage vendors, and balance innovation with safety, compliance, cost, and reliability.

Build and ship production machine learning systems for content abuse detection, including data pipelines, classifiers, LLM evaluations, inference infrastructure, monitoring, and model evaluation. Optimize precision, recall, latency, cost, and throughput while partnering with engineering and data teams. Develop moderator intelligence features, build training and serving platforms, establish metrics infrastructure, and mentor teammates. The role requires end-to-end ownership in a startup environment, from messy data and feature engineering through deployment and production monitoring.

Lead and mentor ML engineers and data scientists, overseeing ML strategy and development of scalable models for healthcare applications.

The Senior Staff Machine Learning Engineer will improve Coupang's search and discovery systems using advanced algorithms, addressing bottlenecks, proposing solutions, and leading projects involving personalization and recommendations.

The Machine Learning Engineer will conduct data analysis, develop ML applications, manage data transformations, and support API and ETL implementations.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

Develop shared customer representations from longitudinal transaction, sales, and interaction data. Define modeling objectives, train and evaluate encoder and embedding models, assess downstream value, and deliver production-ready machine learning systems. Responsibilities include representation learning, temporal modeling, inductive representations, robust evaluation, leakage and cold-start analysis, calibration, drift monitoring, subgroup performance, and research-to-production deployment. Present findings and recommendations to senior stakeholders while addressing privacy, fairness, and re-identification risks.

Build and operate production machine learning systems focused on multilingual NLP, including entity linking, classification, event extraction, deduplication, and narrative analysis. Develop GPU-accelerated workflows, fine-tune transformer models, and create LLM features such as RAG pipelines and agents on AWS Bedrock. Establish evaluation frameworks, integrate ML services into Django and event-driven platforms, lead technical architecture discussions, and mentor team members.