Top Machine Learning Jobs

As an ML Engineer II, you will develop manipulation systems for mobile robots, building models, training datasets, and deployment pipelines for improved robot interactions in dynamic environments.

The Machine Learning Engineer will design, build, evaluate, and deploy ML systems, collaborating on projects in safety, security, and intelligence applications, while owning projects from initial research through to production.

Design, train, and optimize high-performance ML/AI models and inference pipelines for remote sensor (OPIR) data. Integrate models into production Linux-based systems, collaborate with C++ and systems engineers, apply rigorous software engineering and Agile practices, and support missile warning, defense, and sensor-fusion applications.

Lead design, build, and productionize scalable ML systems on cloud platforms. Provide technical leadership, mentor teams, translate business needs into data-driven solutions, work with large datasets, implement recommender/NLP/vision models, leverage LLMs and agentic architectures, and support business development and client engagements.

Design, develop, and productionize scalable machine learning and data science solutions for clients. Analyze very large datasets across cloud platforms, implement recommender/NLP/computer vision and LLM-based systems, optimize for performance and scalability, leverage containerization and data warehousing, and lead/mentor junior practitioners while supporting business development and client strategy.

Lead design and delivery of end-to-end, production-scale ML systems on cloud platforms. Provide technical leadership, mentor teams, analyze and transform large datasets, implement recommender/NLP/risk models, productionize scalable ML, and grow practice through client-facing activities and business development.

Design, build, and productionize scalable machine learning systems; analyze and transform large datasets; implement recommender, NLP, and other analytic solutions; deploy models in cloud/enterprise environments; collaborate with clients and lead junior team members while contributing to business development.

Define and execute predictive modeling and ML strategy for credit risk, servicing, and collections. Lead cross-functional initiatives, oversee model development, deployment, and monitoring, advise senior leadership on data strategy, represent analytics in regulatory and audit forums, and mentor peers while fostering organizational partnerships.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

Develop and lead research and product applications for LLMs, multimodal models, generative AI, and computer vision across cloud, devices, sensors, and robotics. Responsibilities include designing scalable and efficient models, optimizing them for resource-constrained hardware, evaluating real-world performance, mentoring scientists, collaborating with engineering teams, and contributing to patents, publications, and technical documentation.

Build and scale machine learning infrastructure, training systems, inference workflows, and agentic AI tools for protein design research. Improve distributed training performance, GPU utilization, reliability, and scalability while partnering with AI scientists and protein engineers. Translate research prototypes into reusable production systems, contribute to technical direction, evaluate emerging ML tools, and support scientific communication.

Lead end-to-end design, delivery, and lifecycle management of production AI/ML solutions for strategic clients. Translate business requirements into scalable, secure ML architectures, drive deployments, monitoring, governance, and mentor teams while collaborating with stakeholders across sales, engineering, and data science.

Design and scale machine learning infrastructure for personalization, search, ranking, advertising technology, and real-time learning at consumer scale. Build distributed systems, data pipelines, feature infrastructure, model-serving systems, and low-latency decision systems. Lead architecture and zero-to-one initiatives, partner across product and engineering teams, mentor technical leaders, improve experimentation velocity, and explore LLM applications for personalization and conversational search.

Design, build, and optimize Python backend services, APIs, integrations, and data pipelines supporting intelligent agent workflows. Implement LLM, embeddings, RAG, and vector database solutions using AI/ML frameworks and external APIs. Improve deployed agents through prompt engineering and fine-tuning, while contributing to testing, CI/CD, monitoring, architecture, documentation, and enterprise deployment reliability.

Build and productionize ML models for shipping cost estimation, retailer credit underwriting, SEO-driven landing pages, personalization, and lifetime value prediction. Collaborate with data scientists, engineers, and product managers to design features, deploy explainable and performant models, and extract value from two-sided marketplace data to grow and optimize Faire's retailer business.

Deploy and maintain state-of-the-art ML models (LLMs and vision) in production for government customers. Improve models via retraining, tuning, and architecture updates; build scalable ML infrastructure; prototype ML-driven product enhancements; work with massive datasets and multimodal fine-tuning pipelines; collaborate across research, product, and engineering. Requires active security clearance and light travel (~10%).

Build and productionize ML systems for de-identification: improve NER, entity resolution, extraction, and document understanding. Drive error analysis, dataset creation, experiments, evaluation, safe rollouts, inference optimization, and active-learning/LLM-assisted pipelines to improve precision, recall, and utility in live pipelines.

Build, optimize, and deploy production-grade machine learning systems for sports analytics. Improve data pipelines, feature engineering, model training, evaluation, and low-latency prediction services. Collaborate with DevOps and Data Engineering to scale workloads on Kubernetes, maintain cloud-native EDW/ETL solutions, and promote software development best practices.

Design and implement scalable AI/ML-driven HealthTech microservices, contribute to technical architecture, write high-quality code, perform code reviews, develop proofs-of-concept, troubleshoot production issues, and document work while collaborating in agile teams.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The role involves developing scalable ML algorithms, optimizing LLM inference performance, managing model serving pipelines, and collaborating with data engineers for efficient knowledge graph generation.

This role involves bridging research and applied engineering, overseeing training infrastructure, optimizing workflows, and setting technical strategies for AI/ML deployment at zaimler.

The Machine Learning Engineer will build tools for interpretable AI, optimize infrastructure, and integrate workflows into products, ensuring reliability and performance.

Lead design and implementation of production ML systems for low-latency ad use cases (bidding, ranking, pacing, fraud). Build training and feature pipelines, real-time inference services, and ML team. Partner with engineering and product, own model experimentation, evaluation, monitoring, and deployment at scale.