Top Machine Learning Engineer Jobs

Own and architect Garner's production ML platform, ensure reliability, performance, cost-efficiency, implement ML CI/CD, drift monitoring, observability, and set standards for future MLOps team.

As a Senior Machine Learning Engineer, you will design and deploy computer vision models, collaborate with teams, and mentor juniors while ensuring high-performance solutions.

The role involves developing ML systems, designing low-latency infrastructure, and collaborating on AI/ML initiatives while ensuring reliability and performance at scale.

Lead research on machine learning validation for robotics and autonomy systems, focusing on simulation, uncertainty modeling, and creating functional prototypes for testing.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

Lead design, training, and evaluation of multi-modal ML perception models (camera, lidar, radar) for fallback autonomy. Define metrics, curate datasets, implement training and optimized inference pipelines for on‑vehicle constraints, and integrate models into production. Partner with safety, systems, and product teams for validation, verification, and deployment. Mentor engineers and promote ML and software engineering best practices.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

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.

Design, build, and productionize large-scale ML models (CTR, retrieval, conversion, recommendation, search). Lead end-to-end experimentation and online A/B testing, improve model serving and pipelines for low-latency scalable inference, translate product requirements into ML solutions, and mentor/lead other ML engineers.

Lead design and deployment of offline 3D reconstruction, lane-line detection, and automatic mapping pipelines from multi-sensor logged data. Develop pose estimation, sensor fusion, and perception models; implement MLOps, data ingestion/curation, and quality measurement. Serve as project lead, mentor engineers, and define analytics and ML standards to scale auto-labeling and map creation.

Design, develop, test, and productize AI/ML algorithms: select and process data, run statistical analysis and ML models, perform test & evaluation and V&V, integrate systems, retrain models, verify model quality, and convert prototypes into scalable solutions.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

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.

The Staff Machine Learning Engineer will design, train, and deploy NLP models, optimize processes, and mentor engineers. The role demands expertise in model training and productionization.

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, train, and deploy high-throughput ML/NLP models and pipelines at consumer scale. Build resilient production model services, migrate ML infrastructure to public cloud (GCP), process massive distributed datasets, integrate AI-driven workflows, and collaborate cross-functionally to embed personalization into Yahoo Mail while maintaining code quality and performance.

Design, build, and maintain scalable self-service data and ML platform tools and workflows. Mentor junior engineers, collaborate with cross-functional teams, implement monitoring/alerts, drive infrastructure improvements, integrate new technologies, and produce technical documentation to support reliable, scalable data products and pipelines.

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.

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

The Principal ML Engineer will design and scale ML infrastructure for scientific discovery, collaborating with AI scientists to enhance model training and deployment processes.

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.

As a Senior Applied ML Engineer at Macroscope, you will design, build, and optimize ML/AI systems, leading the AI Code Review process and ensuring integration with backend systems.