Top Machine Learning Jobs

Define ML strategy and build production ML systems end-to-end (data pipelines, training, evaluation, inference). Own data strategy, integrate ML with product/backend, write production-quality code, and recruit and mentor the ML team while setting technical direction.

Develop and train whole-body control policies for locomotion and manipulation, build scalable simulation environments, design rewards and curricula, own sim-to-real transfer, and run large-scale GPU training experiments.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

Develop and maintain machine learning models for robotics applications, collaborate across teams, and contribute to innovative solutions in AI and mobility.

As a Principal Decision Scientist, you'll utilize machine learning to provide insights and solutions, managing client relationships and leading projects from conception to deployment. You will architect solutions and oversee development, ensuring best practices in coding and model deployment.

Lead the development of ML models for autonomous vehicle behavior planning, mentor junior engineers, and advance machine learning technology for safe navigation.

The role involves applying machine learning techniques to circuit design, analyzing datasets, building and optimizing models, and collaborating with teams on various projects in VLSI and circuit design.

Develop and evaluate quantization and sparsity recipes for LLM inference, run large-scale post-training and quantization-aware experiments, build evaluation and numerics-debugging tools, collaborate with inference framework teams, and publish or upstream improvements to production inference libraries.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

Lead the development of machine learning models for maritime autonomy, focusing on Radar and EO/IR object detection and semantic segmentation. Collaborate on multi-target tracking and SLAM, and optimize models for edge deployment.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

Lead ML engineering team to develop and manage AI/ML models and production systems, ensuring quality, compliance, and collaboration across teams.

Summer intern collaborating on Grid Software (GridOS, AEMS, ADMS) to develop and enhance ML-driven features. Apply electrical engineering and AI/ML (DNNs, GNNs, time-series) to solve power-system problems, contribute to pilots and R&D, and integrate intelligent agent tools for improved grid operations.

The Staff Machine Learning Engineer will develop advanced Document AI models, solve complex modeling challenges, lead LLM fine-tuning, and provide leadership in a collaborative environment.

Lead the design, development, and implementation of machine learning models to solve complex problems, collaborating with cross-functional teams to enhance services and deploy solutions in production environments.

As a Machine Learning Engineer at Shipwell, you'll build and scale AI-powered logistics solutions, maintaining data pipelines and ML infrastructure across the organization.

Summer PhD internship building and deploying production ML models for search, recommendations, and matching. Collaborate with engineers and data scientists on experimentation, model evaluation, monitoring, and documentation to improve product discovery and user experience.

The Senior Machine Learning Engineer will implement and enhance ML solutions, working closely with various business departments to deliver scalable applications and improve MLOps practices.

The Principal Machine Learning Engineer will develop optimization algorithms, enhance LLM deployments, and collaborate with research teams on model performance and compression.

The role involves creating models for machine learning accelerators, analyzing workloads, and collaborating with engineering teams to optimize performance and functionality.

Lead collaboration to improve ML workloads on cloud and self-driving cars, optimize models, and mentor junior engineers.

Analyze workloads for hardware mapping, design machine learning solutions, and collaborate with cross-functional teams on high-performance architecture.

Design, train, optimize, and deploy efficient on-device ML models (audio and vision) for resource-constrained products; maintain Ambiq ADKs; port and optimize customer models; apply model-efficiency techniques; produce customer-facing demos, docs, and benchmarks.

As a Senior Software Engineer, you'll build AI features focused on LLMs and RAG systems, collaborating across teams to ensure robust performance.