Top Machine Learning Engineer Jobs

As a Staff AI/ML Full-Stack Engineer at GM, you'll design and develop end-to-end software products, own technical projects, and mentor junior engineers while focusing on cloud infrastructure and scalable applications.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

Develop and productionize AI features from PoC to scalable deployments. Write production-ready Python code, implement testing and debugging, refine user stories, participate in ethical and security reviews, and collaborate with stakeholders to align AI solutions with business needs.

Build, deploy, and maintain production-grade ML systems across the full model lifecycle. Translate business goals into ML solutions, monitor model performance and drift, perform analyses and experiments, and collaborate with data engineers, product, and domain experts to improve risk assessment and underwriting outcomes.

Design, train, evaluate, deploy, and monitor machine learning models. Prepare and analyze large datasets, improve model performance through experiments, and collaborate with software engineers to integrate ML solutions into production while staying current with ML techniques.

Build and optimize PySpark pipelines to ingest, clean, and transform high-volume manufacturing time-series data. Design end-to-end ML pipelines for feature engineering, training, validation, deployment, and monitoring. Implement and tune anomaly detection and root-cause models, manage model lifecycle (versioning, retraining, rollback), and set up alerts and data quality checks to ensure production reliability.

Design, build, deploy, and operate AI/ML capabilities in AWS GovCloud using FedRAMP-authorized services. Responsibilities include data preparation, model development, evaluation, deployment, monitoring, RAG implementations, embedding/vector search integration, Python development of production services, containerized deployments, CI/CD, and collaboration with engineering and cybersecurity teams to meet mission and compliance requirements.

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.

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

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.

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

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

Design and implement automated ML and integration solutions, including data pipelines, LLM/AI integrations (Amazon Bedrock, Databricks), malware analysis automation, API integrations, feature engineering automation, monitoring, and scaling pilot systems to production while collaborating with analysts and documenting solutions.

Own and build end-to-end matching and ranking systems for out-of-home advertising: design candidate generation, re-ranking, geospatial scoring, data warehouse and feature pipelines, evaluation infrastructure, low-latency ranking APIs, and model monitoring with drift detection and retraining triggers.

Design and deploy scalable production ML systems powering Diffbot's Knowledge Graph. Improve model accuracy via research, feature engineering, and hyperparameter tuning; own end-to-end performance for 60+ ML problems; assess new research for production feasibility; collaborate on training data and error analysis with product and customers.

Build and deploy low-latency, production-grade ML models and end-to-end pipelines for fraud detection. Engineer high-frequency time-series features from massive event streams, maintain automated MLOps/CI-CD infrastructure, optimize runtime scoring performance across distributed systems, and collaborate with infrastructure, product, and data science teams to translate fraud signals into robust algorithmic solutions.

Develop and deploy end-to-end ML solutions for smart manufacturing: build data pipelines, feature engineering, CV and LLM-based models (RAG/agentic workflows), prototype and productionize models, implement monitoring/retraining, and collaborate with cross-functional teams using modern development and CI/CD practices.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Own end-to-end production ML systems for search, recommendations, and ranking: architecture, training pipelines, inference, deployment, monitoring, drift detection, and retraining. Partner with data, product, and backend teams to ship reliable, low-latency ML-driven product experiences and set standards for ML infrastructure and model health.

Design, build, deploy, and maintain AI/ML models for immigration-related datasets in classified AWS environments. Analyze complex data, operationalize models via DevSecOps CI/CD and containerization, present results, and provide security architecture and identity-management technical leadership for a cleared federal customer.

Design, develop, and deploy multimodal vision-language models to interpret manufacturing documentation (drawings, CAD, quality docs). Build annotation tooling, active learning loops, and synthetic data strategies. Create evaluation frameworks, collaborate on AI platform roadmaps, and drive production model health and deployment for factory-scale automation.

Lead research, development, and production deployment of object detection and segmentation models for document, layout, and part understanding. Build annotation tooling, active learning loops, synthetic data strategies, and evaluation frameworks, collaborate on ML roadmap, and improve model accuracy to drive manufacturing automation.

Own the ML lifecycle for LLMs applied to manufacturing data: fine-tune and deploy models for document classification, information extraction, table parsing, and multi-page reasoning; build annotation tooling, active learning, and synthetic data pipelines; develop evaluation frameworks; collaborate on AI platform roadmap and optimize model accuracy and production health.

Design, train, test, deploy, and maintain end-to-end AI/ML systems for production. Select algorithms, architect MLOps pipelines, optimize models for high-throughput text/image/video feeds, and collaborate with engineers and product owners to deliver mission-critical solutions for federal and defense clients.