Top Machine Learning Jobs

The Lead Machine Learning Engineer will design and implement data-driven solutions, provide technical leadership, and deploy machine learning systems for clients.

Lead Machine Learning Engineer at CapTech will design data-driven solutions, implement machine learning systems, and provide technical leadership. Responsibilities include collaborating with clients and teams, analyzing large datasets, and managing ML practices.

The Lead Machine Learning/Data Science Engineer at CapTech designs and deploys machine learning solutions, providing technical leadership and collaborating with teams to meet client needs using large datasets and cloud platforms.

The Machine Learning Engineer designs and implements data-driven solutions, builds scalable ML systems, and collaborates with clients to meet business needs.

The Machine Learning Engineer designs and implements scalable machine learning solutions for clients, analyzing data and optimizing systems, while collaborating with teams and clients.

The Machine Learning Engineer will design and implement data-driven machine learning solutions, collaborate with teams, and optimize scalable systems. Responsibilities include analyzing datasets, deploying analytical solutions, and leading teams.

Machine Learning Engineers design and implement data-driven solutions, build scalable ML systems, and collaborate with cross-functional teams at CapTech.

The Lead Machine Learning Engineer will design and implement scalable machine learning solutions, lead teams, and analyze large datasets using cloud technologies.

Lead Machine Learning Engineer is responsible for designing and implementing data-driven solutions, optimizing and deploying machine learning systems, and mentoring junior staff while working collaboratively across teams.

The Lead Machine Learning Engineer at CapTech designs and implements scalable machine learning systems, collaborates with teams, and provides technical leadership while optimizing enterprise data-driven solutions.

The Machine Learning Engineer designs and implements scalable solutions for clients, analyzing datasets, productionizing ML systems, and leading teams.

The Machine Learning Engineer designs and implements data-driven solutions, develops scalable ML systems, analyzes datasets, and leads client collaborations.

Lead the scientific direction and machine learning development for climate modeling. Manage model training, evaluation, and collaboration across teams to develop predictive systems based on hydrology and climate data.

Responsible for designing and implementing reinforcement learning algorithms, conducting experiments, and optimizing models for robotic applications, collaborating with multiple teams.

Lead design and production of next-generation search and personalized recommendation systems using LLMs, dense retrieval, transformers, GNNs, and MLOps. Drive model development, GPU-based deployment, and <100ms low-latency ranking while mentoring applied scientists and MLEs and collaborating cross-functionally to productize search experiences.

Design and deploy agentic AI systems, build ML pipelines, optimize production models, and lead enterprise data standardization and governance initiatives. The role collaborates across business and technical teams to identify AI use cases, establish data quality and metadata standards, promote data literacy, advise on AI best practices, and ensure scalable, reliable, compliant AI solutions.

Research and develop multimodal perception and authentication systems using visual, audio, and sensor data. Build specialized and multimodal models, design training and evaluation methods, analyze robustness and failure modes, and integrate capabilities into real-time, resource-constrained systems. Collaborate with hardware, firmware, software, and product teams to transition research into practical products.

Design and architect recommendation algorithms, build data pipelines and continuously learning training jobs, improve ranking systems through real-time experimentation and user feedback, and ensure machine learning systems are scalable and efficient. The role requires industrial-scale recommender systems or deep learning experience and proficiency with data infrastructure and deep learning frameworks.

Lead AI/ML-driven automation for ASIC and SoC design implementation. Explore and prototype generative AI, reinforcement learning, graph neural networks, and convolutional network solutions for VLSI optimization, focusing on power, performance, area, and quality. Collaborate with academic researchers and cross-functional architecture, design, verification, and implementation teams to evaluate methodologies, develop CAD tools, and pilot automation across advanced automotive processor and microcontroller designs.

Develop and lead evaluation systems, benchmarks, and large-scale data pipelines for Waymo foundation models and embodied AI agents. Evaluate models across pretraining, supervised fine-tuning, and reinforcement learning for quality, safety, and realism. Collaborate across teams to bring innovative machine learning technologies into production. The role requires strong ML engineering, deep learning, distributed systems, programming, analytical, and debugging skills.

Entry-level ML engineer to assist in developing, training, deploying, and monitoring machine learning models. Responsibilities include data cleaning, building pipelines, supporting production deployments, troubleshooting model performance, collaborating with team members, and documenting code and processes.

Own machine learning systems end to end, from data exploration and modeling through production deployment, monitoring, and iteration. Build scalable NLP, LLM, retrieval, ranking, recommendation, prediction, and agentic systems with explainable outputs. Partner with Product and Go-to-Market to deliver customer-facing AI capabilities, improve ML platform reliability, communicate technical work clearly, and mentor engineers.

Design, develop, and deploy scalable machine learning models. Collaborate with cross-functional teams to integrate models into production, analyze large datasets, optimize model performance, document work, and stay current with AI advancements.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

The Head of ML will define the technical vision, mentor a team of ML engineers, and lead the production of automation products using survey data.