Top Senior Machine Learning Jobs

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Own and improve Tessellate’s physician-to-organization matching engine by evolving rules-based matching toward semantic matching, embeddings, and vector search. Establish precision, recall, and evaluation practices; resolve healthcare organization identities across millions of records; and maintain a reliable, lightweight data platform. The role requires strong Python and SQL, applied machine learning expertise, production recommendation or ranking experience, and end-to-end ownership in an early-stage startup.

Build and operate production search and indexing systems at billion-vector scale for multimodal driving data. Develop embedding-based retrieval, metadata filtering, backend APIs, ingestion pipelines, and evaluation frameworks covering relevance, recall, latency, scalability, freshness, and cost. Own components from architecture through production operations, collaborate across ML and data teams, make build-versus-buy decisions, and mentor engineers.

Design and productionize machine learning solutions that predict and optimize GPU-accelerated Apache Spark workloads. Develop adaptive ML systems, AI agents, and optimization tools; collaborate with customers and partners on complex deployments; track advances in ML systems; and mentor engineers in data science and machine learning. The role requires expertise in large-scale data processing, modern ML/DL methods, LLMs, generative AI, reinforcement learning, online learning, feature engineering, and boosted-tree models.

Design and deploy production-grade machine learning models and data pipelines across image, video, text, geospatial, time-series, and structured data. Collaborate with customers, data scientists, software and DevOps engineers to define requirements, maintain mission-critical deployments, and contribute to the Chariot AI operations platform.

Architect and develop scalable AI-driven IVR, conversational, NLP, and LLM solutions for high-volume customer interactions. Build classification and clustering models, perform SQL-based analysis, design RAG and agentic workflows, and establish LLMOps practices for monitoring and lifecycle management. Partner with platform, speech engineering, and product teams while mentoring junior and offshore engineers.

Architect, train, and optimize multimodal deep learning models for autonomous vehicle perception. Build large-scale data mining, active learning, hard-example curation, and auto-labeling pipelines. Develop high-resolution vision and spatial-temporal transformer architectures, use foundation models for data curation and synthetic edge cases, and optimize models for onboard accelerator inference.

Develop, evaluate, and release learned driving policies for autonomous vehicles, particularly complex navigation and yielding scenarios. Bridge multimodal perception and planning outputs into safe behavioral actions, evaluate models in closed-loop simulation against safety metrics, and transition production-ready models into autonomous navigation systems. The role also advances data-driven policies, foundation-model experimentation, scenario generation, and end-to-end driving architectures.

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

Design, develop, validate, and deploy scalable AI/ML and simulation/optimization solutions using structured and unstructured data. Lead model development within model risk frameworks, mentor junior scientists, collaborate with engineering and product teams for MLOps/cloud deployments, and communicate actionable insights to business partners while ensuring regulatory and risk compliance.

Architect role to design full-stack, large-scale solutions informed by leading tech companies. Requires deep ML/AI and large-data analysis knowledge, proficiency across languages/frameworks (Go, Scala, Python, R, MATLAB, Node.js, React), NoSQL expertise, and familiarity with mobile testing, deployment pipelines, and SRE practices. US work authorization required.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Lead the development of pricing algorithms for vehicle valuation at CarMax, managing a team of data scientists to implement strategies and maintain optimal pricing models.

You will lead and manage a team of engineers developing machine learning solutions for perception product challenges in autonomous driving.

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.