Top Machine Learning Engineer Jobs

Oversee machine learning projects, build ML pipelines, deploy models, collaborate with teams, mentor engineers, and ensure data quality.

Design, build, and deploy scalable ML systems (NLP, Computer Vision, Generative AI); architect ML infrastructure and pipelines; implement MLOps, CI/CD, monitoring, model optimization, and distributed training; collaborate cross-functionally to productionize models and improve dataset quality.

Develop machine learning models and pipelines for fish farms, interacting with databases, conducting data analytics, and building statistical models.

As a Machine Learning Engineer, develop ML models, data pipelines, conduct analytics, and work on image and video inference for fish health and behavior.

The role involves building and deploying machine learning solutions, collaborating with stakeholders, and ensuring production-ready systems, with responsibilities across the ML lifecycle.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

As a Machine Learning Engineer, you will apply ML techniques for perception tasks, develop model training methods, and evaluate performance in autonomous driving applications.

Design and build foundation-scale datasets and training recipes; develop human and machine labeling methods; create continuous data intake and automated data collection/model update pipelines; evaluate real-world model performance and detect regressions; maintain ground-truth-free performance metrics.

The Senior Data Engineer will develop and deploy machine learning models, build end-to-end ML pipelines, optimize performance, and collaborate across teams to enhance AI capabilities at ClickUp.

The Senior AI/ML Engineer will design, build, and deploy Generative AI and ML platforms, ensuring compliance and high performance. Responsibilities include developing foundational services, overseeing model training infrastructure, and mentoring junior engineers.

Lead the development and optimization of search algorithms and systems while applying machine learning to enhance search relevance and performance.

The role involves transforming research into products, building diverse datasets, accelerating ML workflows, and collaborating effectively across teams.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

As a Senior ML Ops Engineer, you will bridge Data Science and Engineering to automate ML workflows, manage CI/CD for ML systems, and collaborate with various teams to implement solutions for AI features and services.

The role focuses on leading AI/ML architecture and deployment for scalable solutions in cloud security, shaping technical strategies, and managing the lifecycle of AI models.

The role involves building data infrastructure for ML models, managing petabytes of camera data, and collaborating with cross-functional teams to enhance model quality.

The Senior Machine Learning Engineer will design and optimize machine learning pipelines for AI video models, collaborating across functions and translating model performance into business insights.

The role involves developing ML platforms for drug discovery, optimizing data pipelines, and deploying generative models. Candidates need significant ML engineering experience and software skills.

As a Senior Machine Learning Engineer, you will build and productionize models, develop scalable ML infrastructure, and collaborate with teams to optimize model performance while mentoring others.

Design, build, and optimize infrastructure for data and modeling in ML ecosystems, enabling experimentation and development of advanced neural models.

The Senior ML Operations Engineer is responsible for building and maintaining ML infrastructure, model deployment processes, and collaboration with data teams to optimize predictive modeling solutions.

The Senior Machine Learning Engineer will design evaluation frameworks, develop synthetic data pipelines, build evaluation systems, and collaborate with teams to improve model quality in healthcare AI.

The Machine Learning Engineer designs and maintains AI solutions for drug discovery and development, collaborating with teams to enhance pharmaceutical processes while ensuring compliance with regulations.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.