Top Machine Learning Engineer Jobs

The role involves developing advanced AI solutions for e-commerce scenarios, designing algorithms, optimizing ML systems, and collaborating cross-functionally to drive innovations that enhance user experience.

The Senior Staff Machine Learning Engineer will lead innovative AI engineering projects and optimize algorithms for e-commerce applications, collaborating across teams to drive significant business impact.

The role involves designing and building AI components for a platform, specifically focusing on NLP and ML for catalog mapping and anomaly detection, along with feedback mechanisms.

Develop deep learning models for autonomous vehicle trajectory generation, collaborate with cross-functional teams, and analyze system performance metrics.

Lead the 3D Mapping team in designing ML models and algorithms for 3D perception and mapping, coordinating with cross-functional teams and driving development metrics.

The AI and ML Engineer will develop and maintain AI/ML models, collaborate with teams to deliver solutions for national security, and apply algorithms.

The role involves building and deploying machine learning models, collaborating with engineers and product teams, and enhancing customer experience through ML solutions.

Design, build, and deploy scalable AI and machine learning solutions, leading full AI lifecycle, collaborating with teams, and ensuring systems align with client needs.

The Machine Learning Engineer will design, develop, and implement advanced recommendation systems, collaborating with cross-functional teams to enhance personalization for a global audience.

The Principal AI/ML Engineer leads complex model development, optimizes data pipelines, collaborates with data science teams, and contributes to strategic AI initiatives.

As a Staff Computer Vision AI/ML Engineer, you will lead a team to develop and deploy AI/ML algorithms in remote sensing, including analyzing datasets and training deep learning models.

Build and improve state-of-the-art agent systems: design RL/RLHF learning pipelines, create custom environments, set up robust evaluation, train smaller task-specific models, leverage open-source text and image models, and help shape technical and hiring strategy alongside founders.

Design, build, and deploy Azure-based AI/ML pipelines for data migration reconciliation: anomaly detection, exception classification, LLM evaluation, automated data-quality scoring, production model monitoring, and dashboards. Mentor team members, produce documentation, and transfer knowledge to embedded staff in regulated environments.

Design, build, deploy, and maintain production ML models; develop and optimize data pipelines and feature engineering workflows; work with structured and unstructured data (tabular, text, images); translate business problems into scalable ML solutions; support model lifecycle, performance, and cloud-based deployments on Azure, AWS, or GCP; integrate models into applications and enterprise workflows.

Design, build, and operationalize reproducible AI/ML pipelines and automation for mission-critical systems. Troubleshoot CI/CD and containerized workloads, integrate AI capabilities with infrastructure and security, and support resilient hybrid compute operations to enhance warfighting decision support.

Design and deploy end-to-end machine learning solutions: gather/process large datasets, build predictive and topic-modeling systems, develop data pipelines, use AWS and NLP APIs, containerize and deploy models, lead technical teams, and manage project delivery and stakeholder communication.

Lead design, build, and operation of end-to-end production ML systems for ranking, rewards, ROAS/LTV prediction, personalization, and offer optimization. Own feature pipelines, training, inference, experimentation, monitoring, retraining, and MLOps. Partner with Data Engineering, BI, Product, and Engineering to connect ML to business outcomes, mentor engineers, and drive an AI-first engineering culture.

Design, implement, and optimize real-time computer vision and machine learning models for an autonomous anti-drone turret. Integrate algorithms with embedded hardware, collaborate with electrical engineers, and test/validate performance across environments while hardening prototypes into military-grade systems.

Lead development and optimization of real-time computer vision and ML models for an autonomous gun turret, integrate algorithms with embedded hardware and sensors, validate performance across scenarios, mentor junior engineers, and help harden prototypes for military-grade deployment.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Design, develop, and architect scalable, fault-tolerant cloud AI/ML services and pipelines. Contribute to R&D, integrate with emerging frameworks, operate and troubleshoot production systems, build automated data preparation/annotation workflows with secure storage, and fine-tune, train, validate, test, and deploy models. Implement vision-based AI including automated labeling, object detection, and image segmentation while collaborating with a remote global team.

Design, build, productionize, and maintain large-scale ML models and infrastructure using structured and unstructured data. Perform feature engineering, scale models and systems, run experiments, collaborate with researchers, and ensure code quality through reviews.

Design, develop, and productionize scalable ML and deep learning models for compliance use-cases using large structured and unstructured data. Build ML infrastructure and feature engineering pipelines, run experiments and tuning, collaborate with researchers, perform code reviews, and deploy models in distributed, big-data environments.

Design, build, and deploy scalable GenAI/LLM-driven compliance solutions (RAG, embeddings, agentic frameworks). Lead projects, produce production-ready code, mentor engineers, collaborate with compliance and legal stakeholders, and ensure regulatory-aligned, monitored ML systems.

Support climate and Earth system researchers by developing, deploying, and optimizing AI/ML workflows on HPC and GPU-accelerated systems. Assist with compiling, profiling, tuning, and porting scientific applications; optimize system utilization (CPU/GPU, memory, storage, I/O); maintain scientific software environments and datasets; provide consulting, training, documentation, and grant support to advance AI-enabled climate research.