Top Machine Learning Engineer Jobs

Lead design, development, and deployment of advanced machine learning models and infrastructure. Own architecture, data pipelines, experimentation, validation, and team technical leadership to drive AI-driven product features and business value.

The role involves designing and optimizing recommendation systems for Fortnite's Discover experience, including model implementation and A/B testing.

Design and optimize recommendation systems using deep learning approaches, manage pipelines and evaluate A/B tests to improve user experiences.

Optimize large-scale ML training and inference workloads by profiling bottlenecks, implementing efficiency improvements (parallelism, mixed precision, compilation), building observability and benchmarking tools, and collaborating with research and platform teams to scale models and increase training throughput.

The role involves improving AI agent quality through designing experiments, maintaining evaluation infrastructure, optimizing LLM inference, and collaborating with product teams. Responsibilities include end-to-end ownership from research to deployment in a dedicated ML Engineer context.

The Senior Machine Learning Engineer will design, build, and deploy scalable ML systems, collaborating closely with teams to optimize AI processes and mentoring juniors.

As a Senior Machine Learning Engineer, you will develop advanced machine learning and deep learning models and platforms for optimizing advertising performance and conduct complex experiments.

Architect and manage ML pipelines for training and deployment on cloud platforms, automate CI/CD, and ensure system performance and availability.

Design, operate, and scale cloud-native ML infrastructure across GCP and AWS (GPU/TPU), build CI/CD for models, maintain low-latency real-time inference systems, define observability and monitoring for ML models, participate in on-call incident response, and partner with data scientists to improve MLOps and platform usability.

Design and operate production ML systems for large-scale streaming data, ensuring reliability and performance while collaborating with researchers to integrate models into distributed systems.

Develop and optimize lightweight machine learning models for edge AI applications, integrating into production systems and mentoring junior engineers.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

As a Senior Machine Learning Engineer, you'll lead the development of a neural welding simulator, integrating research and production to enhance robotic systems. Responsibilities include building predictive models, collaborating with reinforcement learning engineers, and translating prototypes into maintainable production code.

Develop, maintain, and enhance software systems focusing on data pipelines, machine learning analytics, and cloud technologies while integrating models within defense systems.

The Staff MLOps Engineer will own the AI/ML platform, focusing on the Synthetic Data Platform, overseeing the ML lifecycle, building infrastructure, and mentoring engineering teams while ensuring cost-effectiveness and system performance.

Design, build, and operationalize production-grade NLP and GenAI models (summarization, semantic search, classification, prediction). Develop scalable data pipelines and distributed systems, deploy models with cloud-native tooling, mentor engineers, collaborate cross-functionally, and ensure model monitoring, evaluation, and responsible-AI practices to deliver business-aligned insights.

The Principal Systems Engineer will provide systems engineering and project management for AI/ML architectures, coordinate program management activities, and improve processes within an agile development environment.

Lead Responsible AI projects to detect, mitigate, and evaluate bias across GenAI, foundation models, and recommender systems. Collaborate with product and platform teams, deploy fairness interventions at scale, mentor junior engineers, and define technical strategy to align models with Pinterest policies and business metrics.

Develop and prototype large-scale text-to-image generative models (Pinterest Canvas), collaborate on multimodal embeddings, collect and prepare visual training data (including RLHF and fine-tuning), ship models to production, publish research, and mentor junior researchers.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

Lead technical design and execution for an AI/ML mission platform: drive architecture decisions, lead major engineering epics, simplify distributed systems, optimize edge and cloud performance, mentor engineers, integrate ML models and inference pipelines, and represent technical capabilities to DoD stakeholders.

Lead and build an ML Acceleration team to define strategy and implement scalable solutions to speed ML training. Hands-on role combining technical design (distributed training, hardware and model optimization) with people management, cross-functional collaboration, impact measurement, and continuous performance improvements for production autonomous vehicle ML systems.

The role involves leading compiler engineering, innovating compiler infrastructure and optimization algorithms for ML performance, and working with various teams to implement solutions.

Design, implement, deploy, and monitor production-grade ML and computer vision models (CNNs, transformers, vision-language). Evaluate models, analyze data, improve robustness, support MLOps/CI-CD, and mentor junior engineers within an Agile team.