Top Machine Learning Engineer Jobs

Lead on-device ML inference deployment and performance optimization for outdoor camera/doorbell workloads. Optimize latency, throughput, memory, power and stability across CPU/DSP/NPU/GPU. Perform kernel/operator-level tuning, runtime integration in C/C++, quantization validation, and build profiling/benchmarking tooling. Drive cross-functional work with ML, firmware, and hardware teams and provide staff-level technical leadership and mentoring.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions, including LLM applications, multi-agent systems, agentic workflows, and enterprise automation. Own the AI solution lifecycle in cloud environments, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders to deliver enterprise-scale solutions.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM applications, multi-agent systems, agentic workflows, RAG solutions, and automation using cloud AI platforms. Own the full AI solution lifecycle, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM orchestration, RAG applications, agentic workflows, multi-agent systems, and automation using cloud AI platforms. Own the full AI solution lifecycle, establish architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Design and implement LLM‑powered and agentic workflows for intelligence missions. Capture user workflows, integrate AI with APIs and enterprise data, ensure production reliability and security in classified environments, and collaborate with operators and mission leadership to deliver AI-enabled decision support.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

Develop and deploy production machine learning models and sensor-fusion algorithms for autonomous-truck ego-motion estimation and localization. Build scalable PyTorch training workflows, analyze large vehicle datasets, and create robust C++ and Python production software. Define validation strategies, optimize performance under real-time constraints, make architecture decisions, and collaborate across autonomous-driving teams. Provide technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Design, develop, and maintain core ML inference platform components including model deployment, optimization pipelines, and benchmarking/simulation workflows. Collaborate with systems and cloud engineers, and build APIs/tools to ensure scalable, reliable, and hardware-efficient inference solutions.

Design and optimize production machine-learning runtime pipelines for autonomous vehicle motion planning. Build high-performance C++ inference, feature extraction, trajectory generation, and orchestration components for embedded platforms. Develop validation, safety guardrails, fallback mechanisms, testing, monitoring, debugging, and observability tools. Investigate complex runtime failures and optimize latency, throughput, memory, and multithreaded performance while collaborating with machine-learning and systems engineering teams.

Develop and implement machine learning algorithms for defect detection in high-resolution images. Optimize real-time performance and evaluate model efficiency.

The role involves training and developing large-scale image and video models, optimizing data pipelines, and conducting experiments in a high-performance computing environment.

The ML Engineer will architect and build personalization and recommendation systems, design user preference algorithms, collaborate with teams, and improve systems from research to production.

The Machine Learning Engineer will apply data science and machine learning practices to solve problems, develop prototypes, and communicate insights to stakeholders in a dynamic environment.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

Develop machine learning and generative AI methods to optimize chip layout for performance, power, and area. Automate physical design, verification, and exploratory PDK generation; build benchmarking, modeling, and QA systems; and evaluate advanced device architectures and process technologies for sub-2nm nodes. Collaborate with global R&D organizations and research consortia, transfer technology to Samsung’s Korea-based development teams, and guide cross-functional technical initiatives.

As an intern, you'll develop software and machine learning systems for robots, focusing on perception, intelligence, behavior, and real-time software integration.

The ML Engineer will develop and advance multimodal models for robot perception and behavior, integrating these systems into hardware while collaborating closely with robotics engineers.

Lead the development of machine learning algorithms focusing on perception for autonomous driving, collaborating across teams and mentoring ML developers.

The Principal Machine Learning Engineer will fine-tune and optimize LLMs for various applications, develop AI products, and enhance ML infrastructure in collaboration with cross-functional teams.

As a Principal Machine Learning Engineer, you will architect AI solutions, collaborate with scientists, and optimize models for edge deployment. Your work enhances public safety applications.