Top Machine Learning Engineer Jobs

The Senior AI Engineer will develop AI solutions for incident management, collaborate cross-functionally, and enhance observability tools through iteration and ownership.

Design, build, and maintain ELT pipelines and analytics-ready data models to support reporting, KPIs, and downstream ML use cases; implement data quality, optimize pipeline performance, document lineage, and ensure governance and privacy.

Design and maintain datasets, build and evaluate domain-specific ML and vision-language models for real-time privileged access threat detection; deploy and optimize Python/Docker inference services integrated with WebSocket/WebRTC and protocol-level interfaces; monitor and document production models.

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

Develop perception systems for autonomous vehicles, train deep learning models, and work with multi-sensor data to enhance model reliability and performance.

As a Machine Learning Engineer at FanDuel, you'll develop scalable ML applications, enhance user experience through intelligent systems, and collaborate across teams to validate AI insights.

The Machine Learning Engineer will design and implement search systems, develop scalable serving systems for ML, and collaborate with teams to productionize AI insights, requiring engagement with big data and machine learning frameworks.

The Senior Machine Learning Engineer will build and operate ML systems end-to-end, focusing on production-grade ML platforms, feature pipelines, and MLOps practices while collaborating with cross-functional teams.

The Senior Data Engineer defines technical approaches for client projects, evaluates modeling strategies, and conducts internal R&D in applied machine learning and analytics, with a strong emphasis on Python-based solutions and mentorship of junior engineers.

As an ML Eval Engineer, you'll build evaluation systems for model quality, develop metrics to identify failures, and collaborate with teams to improve models using real-world datasets.

Join Equilibrium Energy as a Staff/Sr Staff Software Engineer to enhance the Science Platform for ML model deployment, requiring strong Python and ML expertise.

The role involves collaborating with various teams to design datasets for AI model training, developing frameworks for data processing, and contributing to data management tools, all while applying strong ML and software engineering skills.

As a Senior Machine Learning Engineer, you'll design and implement ML models for personalization, collaborate across teams, and enhance recommendation systems.

The role entails optimizing model performance and developer experience through serverless inference platforms, distributed systems, and API development. Responsibilities include enhancing system robustness and scalability, partnering with product teams, and maintaining clean software and IaC.

Translate RL and inference research into production systems to improve latency, throughput, cost, and model quality. Design and implement inference engines and scheduling, build RL/post‑training pipelines, profile and optimize GPU/network/memory performance, run scale experiments, and drive production roadmap and benchmarks. Provide technical leadership and mentor others.

Lead and scale enterprise ML infrastructure and deployment pipelines, own end-to-end model lifecycle (development to production), drive MLOps strategy, ensure production reliability and compliance, mentor teams, and collaborate with clinical and product stakeholders.

Design, build, and maintain machine learning systems and fraud-detection solutions. Collaborate with product and engineering teams, perform code reviews and testing, optimize performance and maintainability, and deliver data-driven business solutions while producing technical documentation and ensuring security and architectural compliance.

The Software Engineer III designs and develops complex software solutions, provides application support, and improves operations through systems analysis and recommendations.

Build and maintain ML infrastructure to accelerate model development and deployment: scale model evaluation, optimize GPU utilization, automate staging/deployment, migrate workflows to orchestration tools, and improve Python monorepo tooling and CI/CD.

Work with sales and partners to demonstrate Snowflake, run proofs-of-concept, design solutions, and close large enterprise deals. Present to technical and executive audiences, collaborate with product and engineering, and maintain deep competitive and industry knowledge.

Design, prototype, and deploy machine learning models and pipelines to detect environmental changes for Lyft's map. Collaborate with engineers, data scientists, and product teams to analyze data, build POCs, write production-quality code, and evaluate ML systems against business goals.

Design and build scalable, fault-tolerant ML serving platforms for robotics. Optimize computer-vision and foundation-model pipelines for cloud GPUs, develop observability and monitoring, conduct architecture reviews, and lead end-to-end projects integrating research and production.

Develop and maintain visual analytics tools for AI/ML workloads, collaborating on visualization solutions and innovating methods to enhance workflows.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.