Top Machine Learning Engineer Jobs

The Staff Machine Learning Engineer will design and build scalable ML systems for health insights, collaborating with cross-functional teams to ensure compliance and quality in production-ready services.

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.

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.

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

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.

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.

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.

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.

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.

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, development, and production deployment of AI/ML solutions and conversational chatbot platforms. Own end-to-end implementation, MLOps/CI-CD, containerized deployments, solution architecture, security and governance, mentor engineers, and collaborate with product, enterprise architecture, and vendors to deliver scalable, monitored AI systems.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

Design, build, and deploy Voice AI solutions (ASR, real-time transcription, bilingual switching, speech processing). Partner on architecture, cloud services, CI/CD, HIPAA-compliant model lifecycle, and enterprise vendor evaluation. Translate research into production-ready, reusable capabilities and drive AI standards.

Own and operate models in a production AI research platform: select and fine-tune models, build training/evaluation/extraction pipelines, implement eval harnesses and CI, redesign model routing for accuracy/cost/latency, and drive end-to-end product-quality and verification for finance workflows.

Design, build, test, and deploy production AI/ML solutions and full‑stack applications (React/Next.js, Python, PostgreSQL). Integrate LLMs and agentic AI frameworks, develop backend services/APIs, collaborate cross-functionally, and optimize reliability and performance within regulated clinical workflows.

Lead design, development, and deployment of large-scale production ML systems and platform strategy. Research and prototype ML and big-data technologies, integrate ML services across the org, mentor engineers and data scientists, and communicate complex ML solutions to technical and non-technical stakeholders.

The role involves designing and implementing AI and machine learning solutions, transforming data into insights, and supporting client engagements. Responsibilities include model development, data analysis, and enhancing model performance using established technologies.

Lead production of machine learning applications, provide technical expertise, and mentor engineers to drive ML model delivery at scale.

As a Lead AI/ML Engineer, you will design and implement scalable AI solutions, mentor teams, and integrate cutting-edge technologies to improve healthcare applications.

Assist in developing and deploying AI and ML features, collaborate with engineering teams, and align AI initiatives with the product vision.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.