Top Machine Learning Engineer Jobs

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.

Design, build, and deploy machine learning models for threat assessment and decision-making in operational environments, collaborating with multidisciplinary teams and managing the ML development lifecycle.

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.

Develop advanced Vision-Language-Action models for robotaxis, leading data strategies and improving driving behaviors using machine learning infrastructure.

Develop and deploy machine learning models for national security challenges. Responsibilities include algorithm development, model training, deployment, and collaboration with teams for effective ML solutions.

The Senior Principal Engineer will lead the development of ML systems at SoundCloud, focusing on recommendation and personalization techniques while mentoring engineers and driving strategic direction.

Lead the design and implementation of scalable AI systems focusing on large language models and speech technologies, while providing technical leadership and mentoring.

As a Machine Learning Engineer, you'll build and enhance features for a search stack, design ML/NLP solutions, and implement an experimentation framework while ensuring code quality.

The ML Engineer will design, develop, and implement machine learning models, manage ML pipelines, integrate APIs, and collaborate with teams to deploy solutions.

The Founding ML Engineer will develop and deploy ML systems, collaborate with teams, and translate ambiguous problems into production ML solutions, shifting between client delivery and platform development.

The Principal AI/Machine Learning Engineer will lead the adoption of AI/ML in manufacturing by defining use cases, driving implementation, and ensuring organizational readiness.

The Machine Learning Engineer will conduct data analysis, develop ML applications, manage data transformations, and support API and ETL implementations.

Design and manage evaluation datasets, develop evaluation pipelines, score assistant quality using LLMs, and build observability for AI agents.

The role involves designing, implementing, and optimizing CUDA-based kernels for autonomous vehicles, ensuring high-performance ML inference for AV software.

Lead end-to-end ML projects for automotive manufacturing: design, train, validate, deploy, and monitor models; implement MLOps/CI-CD; perform data engineering, EDA, and model evaluation; ensure data security and regulatory compliance; document and communicate results to stakeholders.