Top Machine Learning Jobs

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.

Support maintenance and curation of geospatial data holdings, perform ETL and statistical analysis, build visualizations and lightweight analytic tools, automate preprocessing workflows, monitor data pipelines, and advise on imagery exploitation strategies to improve ML model development.

Develop machine learning and computer vision algorithms for automation and autonomy. Collaborate with experts, optimize solutions, and apply research insights.

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

Interns will work on data innovation projects involving data analysis, machine learning pipelines, AI systems deployment, and enterprise AI integration while gaining exposure to Fortune 500 clients.

Develop and productionize ML models for assessing autonomous vehicle behavior, build ML infrastructure, and collaborate across teams for deployment.

The Senior Principal Engineer will lead the development of ML systems at SoundCloud, focusing on recommendation and personalization techniques while mentoring engineers and driving strategic direction.

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.

Train and iterate custom ML models on manufacturing and design data to estimate cost and complexity, generate designs, and extract structured data from engineering documents. Build end-to-end pipelines from data cleaning and labeling through training, evaluation, and deployment. Collaborate with engineering and manufacturing teams to source datasets and identify opportunities to improve workflows with custom models.

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.

Design, train, and deploy machine learning models for detecting malicious content, working with large datasets and collaborating with teams to enhance system scalability.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Develop data-driven solutions using Python and R; perform statistical analysis, data wrangling, and machine learning model development with TensorFlow and Scikit-Learn. Collaborate with cross-functional teams to deliver actionable insights, predictive analytics, and automation. The role also requires software engineering experience, familiarity with agentic AI frameworks or coding agents, and an active TS/SCI clearance with polygraph.

Develop data-driven solutions using Python and R; conduct statistical analyses; design and train machine learning models with TensorFlow and Scikit-Learn; wrangle and prepare raw data; and collaborate with cross-functional teams to deliver actionable insights. The role requires extensive software engineering experience, agentic AI or AI coding-agent expertise, active TS/SCI clearance with polygraph, and a relevant bachelor's degree or equivalent experience.

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 Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

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.