Top Machine Learning Engineer Jobs

Develop, deploy, and optimize machine learning models (object detection, signal processing, predictive analytics) and data pipelines. Perform model evaluation, performance testing, and system-level debugging. Collaborate with engineers to integrate AI/ML into mission-critical systems, document designs, and support validation, verification, and safety/compliance activities.

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).

Develop and maintain ML pipelines for entity classification; collaborate with data scientists; implement monitoring frameworks and improve data workflows.

Lead design, development, and deployment of deep learning models for pathology image analysis. Drive experiments, optimize models for production, support data and ML infrastructure, implement semi/self-supervised methods, collaborate with clinical and product teams, and ensure validated, scalable medical device software.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current with ML/AI advances.

Design, implement, and optimize machine learning and deep learning models; preprocess and prepare large datasets; integrate models into production with software engineers; monitor and maintain production model performance; troubleshoot issues; and collaborate with stakeholders while staying current on ML/AI advances.

Develop, deploy, and maintain deep learning perception models for autonomous delivery drones. Build resource-constrained real-time inference, improve existing models and pipelines, and collaborate with hardware, software, and flight-test teams to productionize safe, reliable perception features.

Early-career ML engineer who builds, evaluates, and improves LLM-powered financial applications. Design and implement ML models, test and analyze LLM failure modes, apply statistics and experimental design, collaborate with cross-functional teams, ensure responsible AI practices, document results, and support junior teammates.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Design, develop, fine-tune, and deploy generative AI models (GANs, VAEs, transformers). Collaborate with cross-functional teams, troubleshoot model issues, document work, and communicate technical concepts to non-technical stakeholders while staying current on research.

Optimize and benchmark deep learning training and inference stacks on HPC/GPU hardware; profile and triage system bottlenecks; build automation tools; develop kernels and systems for new models; lead design reviews, review code, document work, and represent the lab at conferences.

Develop and implement large-scale machine learning and foundation-model systems, collaborate with research teams to integrate technologies, maintain ML lifecycle (data preprocessing, pre-/post-training, evaluation), conduct design reviews and code reviews, debug system issues, contribute to documentation and research publications, and represent the institute at conferences.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

The Senior AI/ML Engineer will design, develop, and deploy scalable ML systems focusing on LLM applications, collaborating with various stakeholders to enhance healthcare data insights.

Design and deliver scalable ML systems from research through production. Lead and mentor engineering teams, define project scope and technical roadmaps, collaborate with product and architecture, and ensure best practices for model development, deployment, and data engineering.

Design, develop, and deploy production 3D and BEV perception ML models for autonomous trucks. Own end-to-end model lifecycle from data prep, training, evaluation, and deployment. Improve model robustness, performance, and generalization across sensor modalities. Integrate models with autonomy software, enhance training pipelines and tooling, analyze failures, and mentor junior engineers.

Design, build, and deploy ML models (NLP, time-series, event detection) and LLM-driven systems; develop low-latency model serving, inference pipelines, CI/CD, monitoring, and retraining on AWS; collaborate with data engineers to produce training datasets, feature stores, and scalable production microservices in Python and Go.

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

Apply deep learning to improve quality and accuracy of labeled multi-modal sensor data for simulation. Build models to identify and correct perception-data issues, analyze data quality and anomalies, collaborate with perception and simulation teams, and lead ML strategies for high-fidelity data generation and labeling pipelines.

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.

Lead a small team to research, develop, optimize, and deploy computer vision AI/ML algorithms for remote sensing (EO/SAR) and ATR. Analyze large datasets, research multimodal fusion, train and deploy deep learning models, review literature, produce technical documentation, and mentor junior engineers while interfacing with program leadership and customers.

Lead design and deployment of scalable auto-labeling pipelines and automated graders. Train, optimize, and productionize state-of-the-art 2D/3D computer vision models and VLMs. Set ML strategy, mentor engineers, and collaborate with Perception, Planner, and Simulation teams to meet labeling needs.

Develop and optimize a high-speed GPU-based simulation framework for ML training and validation. Apply reinforcement learning to solve behavior and path-planning challenges, bridge sim-to-sim and sim-to-real fidelity gaps, enable self-serve data generation for autonomy users, and write production-ready code integrating ML into core simulation architecture.