Top Machine Learning Engineer Jobs

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Build and productionize statistical and ML models to optimize advertiser performance and business goals in mobile ad tech. Tune optimization, model competition and dynamic environments, design experiments, develop budget retention/growth and bidding systems, and collaborate across engineering and business teams while ensuring explainable, well-tested solutions.

Design, implement, and deploy AI/ML models and analytics pipelines for intelligence customers. Analyze large, noisy datasets, build scalable backend services and APIs, and create visualizations and UIs. Collaborate with researchers, engineers, and end users to prototype, harden, and operationalize machine learning solutions across cloud, on‑prem, and edge platforms.

Develop, evaluate, and deploy AI/ML models for national security applications. Analyze complex datasets, build scalable backend services and APIs, create visualizations and UIs, and collaborate with researchers, engineers, and customers to transition prototypes into production across cloud, on-prem, and edge environments.

Design, build, and implement web applications and back-end services. Work with generative AI models and cloud components, ensuring adherence to coding standards and managing project timelines.

The role involves designing and implementing machine learning solutions for geospatial data, participating in multi-discipline teams, and mentoring internal teams while ensuring efficient project execution and client communication.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

Design and implement data pipelines, ensure data quality and scale, build/train/fine-tune machine learning models, deploy and monitor models in production, optimize inference performance, and collaborate with data scientists, engineers, and business stakeholders while communicating results.

Lead design, build, deploy, and monitor scalable ML/GenAI solutions and data pipelines on cloud platforms. Drive technical vision, mentor teams, integrate MLOps/CI-CD, and translate analytics into business impact.

The Senior AI/ML Engineer will enhance developer productivity by improving Python workflows, leading projects, mentoring engineers, and driving best practices.

Lead design and delivery of enterprise AI/ML and intelligent automation platforms. Define architecture strategy, build backend APIs and event-driven pipelines, deploy cloud-native solutions, drive adoption of generative and agentic AI, mentor engineering teams, and ensure security, scalability, and governance across AI initiatives.

Drive hardware-aware ML algorithm development and optimization for ARM compute platforms. Analyze ML workloads (PPA focus), prototype algorithms, and optimize state-of-the-art networks for CPU/GPU/NPU targets.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

Build and productionize large-scale data processing, featurization, and inference pipelines for multimodal and vision models. Improve inference frameworks for reliability and cost-efficiency, drive scalability across compute, storage, and queries, and develop evaluation methods to support rapid experimentation and production deployment.

Design and implement scalable ML infrastructure solutions, mentor junior engineers, own technical projects, and collaborate across teams to solve complex AI/ML problems.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

The Principal AI/ML Engineer will lead the AV ML Infra team at GM, focusing on developing scalable ML infrastructure and guiding cross-functional collaboration while driving technical projects from inception to completion.

The Staff AI/ML Engineer will design and implement scalable ML infrastructure solutions, lead projects, mentor peers, and enhance ML productivity at GM.

Build and operate scalable Golang backend services and APIs for a multimodal ML platform handling video, image, and document processing. Integrate ML/LLM inference, agentic workflows, and vector retrieval (Milvus). Deploy and manage services on Kubernetes with Terraform on AWS, ensure observability, reliability, and security, and collaborate with ML engineers to productionize pipelines and run on-call/incident response.

Designs, trains, evaluates, and deploys ML and AI models; implements MLOps for production, monitors model performance, collaborates with data scientists and stakeholders, and optimizes scalability and reliability.

Provide technical leadership to design, build, and productionize large-scale ML systems; deliver and optimize ML models and data pipelines using distributed and cloud architectures; mentor engineers, evangelize best practices, and collaborate with Product, Architecture, Engineering, and Data Science teams.

Lead cloud migrations for voice contact center solutions, design AI/ML capabilities, improve automation and governance, and ensure production readiness while collaborating with cross-functional teams.

Lead design, build, and deploy production-grade Voice AI and LLM capabilities (ASR, real-time transcription, bilingual switching). Set architecture and enterprise standards, mentor engineers, ensure HIPAA-compliant, secure, and reusable solutions, and align roadmaps with business partners and vendors.

Design, build, and deploy production-grade ML and AI solutions for behavioral health. Architect end-to-end MLOps pipelines, optimize and monitor models in production, evaluate emerging AI trends, collaborate cross-functionally, and provide technical leadership and mentorship to engineering teams while ensuring responsible, scalable, and secure AI deployments.