Top Machine Learning Engineer Jobs

Lead integration of AI/ML into a cloud-native human-performance platform: architect generative AI and RAG workflows, build serverless backends and microservices, optimize data pipelines (SQL/NoSQL, embeddings), maintain CI/CD, modernize legacy PHP backends, mentor engineers, and drive architecture and deployment of LLM-based features.

Build and deploy ranking, matching, recommendation, and allocation ML systems for a global talent marketplace. Work across retrieval, scoring, inference (real-time and batch), feedback loops, and experimentation to optimize long-term hiring outcomes under marketplace constraints.

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.

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.

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.

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.

Own and improve the data infrastructure and core pipelines powering underwriting and claims. Build ingestion and enrichment pipelines for messy claims data, partner with data scientists and actuaries to productionize models and logic, and establish SLAs, monitoring, and on-call practices to ensure reliable, observable systems.

Own and build the data foundation for an AI sports-performance platform: design data architecture, real-time streaming ingestion, time-series storage, feature store and millisecond feature serving, sport knowledge graph and evaluation/calibration frameworks, ensure tenant-level isolation and provenance, and work closely with domain scientists and AI engineers to deliver trustworthy, production-grade ML infrastructure.

Build and operate large-scale data infrastructure for robot learning: ingest, validate, transform, and featurize multimodal data; run batch and streaming workflows; design storage and metadata systems; optimize training-time performance; manage data movement and lifecycle; implement observability and guardrails; collaborate with researchers and engineers.

Build and productionize ML systems for a CTV ad-buying platform: write production Python, train/deploy/monitor models for real-time bidding and optimization, develop incrementality measurement and attribution, use LLMs/generative AI for internal tools, and serve as a technical lead and mentor on a distributed team.

Design, develop, test, and maintain cloud-native, API-driven AI/ML software solutions. Lead technical delivery, design scalable architectures, implement AWS-based deployments, productionize ML/GenAI applications, and collaborate with stakeholders to translate business needs into secure, maintainable systems.

Lead strategy, design, and delivery of decision intelligence and recommendation systems. Architect scalable model ecosystems, set ML best practices, ensure data privacy and observability, partner with product and engineering, and hire and mentor an engineering team to improve performance and user experience.

Design and build scalable backend services and data ingestion pipelines to process and enrich data (including LLM enrichment). Perform data modeling, mapping, and large-scale file manipulation while collaborating in an Agile team to deliver reusable APIs, optimize performance, and improve processes.

The Software Engineer will design and optimize large-scale data infrastructure, focusing on distributed systems and data processing for generative media models, collaborating closely with research scientists and enhancing multi-modal training pipelines.

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 role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

Design, train, and deploy state-of-the-art deep learning models (transformers, CNNs) for end-to-end navigation. Build perception, prediction, and planning solutions, implement data pipelines, deploy and monitor models in production, detect drift, and troubleshoot integration and performance issues for real-world robotic systems.

The role involves designing and maintaining machine learning models, analyzing data, and optimizing algorithms to solve business problems.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

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.

Design and implement large-scale ML systems to optimize ad performance and supply personalization. Conduct data analysis, online experimentation, and causal inference. Build data pipelines and collaborate with engineers, data scientists, and product managers to drive product decisions and deliver scalable ad-serving solutions.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

Design and implement agent harnesses, evaluation benchmarks, and post-training pipelines. Develop RL environments and reward models, run and optimize multi-GPU training, profile and debug training jobs, and iterate on model fine-tuning to improve capabilities for agentic, edge-aware applications.

As a Staff Software Engineer in ML Platform at Stripe, you will lead complex projects, influence technical strategies, and mentor teams to enhance machine learning systems that power Stripe's products.