Top Machine Learning Engineer Jobs

Design and oversee the development of robust data platforms for training and evaluating ML models, focusing on autonomous driving. Collaborate with multiple teams and optimize data processing pipelines.

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

As a Senior/Staff ML Systems Engineer at Quilter, you'll design and maintain ML infrastructure for automated PCB component placement, manage data pipelines, and lead systems design reviews while ensuring project efficiency and reliability within a fully distributed team.

Design, build, and operate secure, scalable cloud-native backend systems for unstructured data and AI workloads. Partner with platform, security, and ML teams to create microservices, APIs, and data pipelines, deploy large-scale ML models, and deliver self-service capabilities while prioritizing privacy, ethics, and reliability.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

Design and implement end-to-end AI agent systems, collaborate with cross-functional teams, and oversee deployment and performance evaluation in production environments.

As a Senior Machine Learning Engineer, develop LLM evaluation frameworks and agentic AI workflows to extract insights from conversations and optimize customer experiences.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

As a Senior Machine Learning Engineer, you will drive AI vision, develop models, and collaborate cross-functionally to enhance user experiences.

Build and expand in-database ML model catalog: implement new model types, ensure framework parity with scikit-learn/Spark ML, optimize training and inference at petabyte scale, extend SQL-level linear algebra, and collaborate across teams to deliver correct, performant ML features and documentation.

Build and maintain ML training and inference infrastructure, model APIs, and design pipelines. Partner with ML and protein design scientists to convert research into reproducible tooling and production services. Optimize compute and storage, implement CI/CD, monitoring, and follow engineering best practices to scale and accelerate protein design workflows.

Lead ML efforts for behavior and planning: design, train, and deploy state-of-the-art models (prediction, decision-making, RL/imitation, generative, transformers), own full model lifecycle from data to onboard inference, generalize behavior across geographies and platforms, collaborate cross-functionally, and mentor engineers.

Lead hands-on research and engineering across agentic ML: train and fine-tune models, design evaluation and observability, build production improvement loops and tooling, prototype agent architectures, partner cross-functionally to productionize research, and set technical direction while mentoring senior engineers and scientists.

Design, build, and deploy production ML systems that combine structured clinical/EHR data with computer vision outputs. Own model calibration, interpretability, monitoring, drift detection, retraining workflows, and regulatory documentation. Partner cross-functionally, mentor engineers, and translate clinical questions into reliable, production-grade ML solutions.

Design, build, deploy, and operate production-grade ML systems and pipelines for a massive consumer audience. Own full lifecycle from data processing and feature engineering through training, validation, deployment, and monitoring. Drive ML infrastructure and architecture decisions, partner with product and data teams, optimize models against business metrics, and mentor engineers to ensure security, reliability, and compliance.

The Research Engineer, Infrastructure will build distributed training systems, optimize performance, manage data pipelines, and enhance research workflows, ensuring the infrastructure scales with AI advancements.

Design, build, and scale ML infrastructure: ingest vehicle sensor data, create batch pipelines for dataset curation, run distributed GPU training, and ensure performance, observability, efficiency, and security across the ML pipeline while partnering with ML teams.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The role involves developing end-to-end machine learning solutions, focusing on NLP and computer vision, collaborating with teams, and preparing technical briefings for stakeholders.

As an Applied ML Software Engineer, you will design and implement ML models, manage cloud infrastructure, and optimize backend systems for AI-driven content discovery and recommendations.

The ML Platform Engineer will build scalable architecture for ML training workloads, optimize system performance, and collaborate with teams for enhanced efficiency on Kubernetes.

Develop machine learning and computer vision algorithms for automation and autonomy. Collaborate with experts, optimize solutions, and apply research insights.

Build and improve ML infrastructure and model pipelines for autonomy: optimize models (quantization, pruning, low precision), maintain and profile GPU ML compilers and runtimes, develop a model compiler framework (FTL), and collaborate with autonomy teams to validate and deploy optimized models to fleet vehicles.