Top Machine Learning Engineer Jobs

The role involves designing and scaling ML infrastructure for LLMs, including building training pipelines and deploying models in cloud and on-prem environments. Collaboration with engineers and managing GPU/TPU workloads is key.

Design and implement ML solutions for optimizing GPU accelerated Apache Spark workloads. Collaborate on deployment and lead technical teams in ML.

The Machine Learning Engineer will apply machine learning techniques to enhance the performance of MIMO radios and wireless systems, collaborating with experts to develop solutions for challenging RF environments.

As a Founding Machine Learning Engineer at Trove, you will develop AI applications for financial firms, focusing on data retrieval and knowledge graphs. Your role involves designing algorithms and collaborating with elite AI labs to enhance human-level capabilities in GenAI.

The Machine Learning Engineer II will design, develop, and maintain machine learning models, lead ML initiatives, collaborate across teams, and mentor junior members while working on Insider Risk Management solutions.

As a Machine Learning Engineer, you'll design and deploy large-scale models for advertising and commerce systems, optimizing for performance and relevance while maintaining ML pipelines.

The Machine Learning Engineer will develop and deploy AI systems for robotics, focusing on advanced manipulation techniques and control algorithms, collaborating across teams.

The Staff ML Engineer will lead architecture decisions for ML infrastructure, operate real-time computer vision systems, and mentor engineers, ensuring high-quality ML deployment and operational excellence at SimpliSafe.

Design and optimize GenAI solutions, collaborate with teams, enhance system performance, present technical findings, and stay updated on GenAI advancements.

Develop, train, and deploy AI surrogate models to enhance engineering simulations for aerospace applications while collaborating with engineers to tackle complex challenges.

Develop ultra-realistic simulations for autonomous vehicles using machine learning, focusing on realism and integration into production systems.

The Machine Learning Engineer will collaborate on AI projects, develop algorithms, optimize solutions, and maintain coding standards within Kitware, contributing to various machine learning tasks.

Collaborate on machine learning projects, perform prototyping, validate algorithms, and troubleshoot code for robustness in AI and computer vision.

Design and deploy next-generation recommendation and ranking models using LLMs, RLHF, and preference learning. Build user-understanding systems, apply RL and optimization to bidding and ad delivery, run causal A/B experiments, and collaborate with engineering to productionize research across the ML pipeline.

Design and evaluate next-generation recommendation and ranking models using LLMs, RLHF, and preference learning. Build user understanding systems for conversion and value estimation at scale. Apply RL and optimization to bidding and real-time delivery. Run causal inference, A/B tests, and offline evaluations. Collaborate with engineering to productionize research and communicate results to technical and non-technical stakeholders.

Design, build, and evaluate next-generation ranking and recommendation models using LLMs, RLHF, and preference learning. Develop user understanding systems for conversion and value estimation at massive scale. Apply reinforcement learning and optimization to bidding and auction dynamics. Design and run causal inference and A/B experiments. Partner with engineering to productionize models and communicate results to technical and non-technical stakeholders.

As a Machine Learning Engineer I, you will develop and deploy machine learning systems for core relevance signals, collaborating with engineers and product teams.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

The Senior Software Engineer will design and optimize recommendation algorithms, maintain data pipelines, and improve personalized user experiences using machine learning techniques and backend engineering skills.

Lead development, deployment, and maintenance of production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to build data pipelines, fine-tune foundation models, apply responsible AI practices, and contribute technical vision for scalable, high-performance AI infrastructure in risk management.

Lead design, build, deploy, and maintain production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to create data pipelines, retrain and monitor models, and apply responsible, explainable AI practices while contributing technical vision and roadmap for AI solutions.

Design, develop, and deploy Generative AI and ML solutions (LLMs, RAG, agentic frameworks) for healthcare claims adjudication. Build end-to-end pipelines, apply MLOps, ensure LLM security and responsible AI, deploy on cloud platforms, integrate data warehousing, collaborate with product and platform teams, and mentor junior engineers.

The role involves developing machine learning models for neural data, collaborating with teams, and exploring new architectures. The candidate should have applied ML experience and strong programming skills.

The AI/ML Engineer will design and deploy machine learning solutions, build model architectures, collaborate with teams, and ensure model performance and scalability.

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.