Top Machine Learning Engineer Jobs

The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.

Design and scale advanced ML models and systems for prediction and recommendation. Mentor junior engineers while leading projects occasionally.

Lead the development of GEICO's AI virtual agent platform, overseeing design, implementation, and collaboration with cross-functional teams, while mentoring junior engineers.

The role involves leading the technical design and architecture of AI/ML platforms, managing cross-functional collaborations, and mentoring engineers while ensuring high performance and reliability of the systems.

The Lead AI/ML Engineer will oversee the design, development, and deployment of AI solutions, manage cross-functional teams, and ensure system reliability and performance in mission-critical environments.

Design and deploy algorithms for gesture detection on embedded hardware, owning the development cycle from design to optimization, while collaborating across teams.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

The Senior/Staff Machine Learning Engineer will design machine learning solutions for robotics, focusing on task planning, model performance, and system integration across various teams.

The Senior AI/ML Engineer will build scalable AI systems, work with LLMs, optimize models, and deploy solutions using AWS while ensuring ethical standards.

As a Machine Learning Infrastructure Engineer, you will build and maintain infrastructure for ML model training, optimize deployment, collaborate across teams, and improve internal tools for ML workflows.

The role involves incorporating machine learning techniques into sensor simulation, working with teams to develop effective models for Lidar, Radar, and Camera systems.

The Staff Machine Learning Engineer will develop and deploy advanced AI models and systems, enhance GenAI capabilities, and collaborate with cross-functional teams.

As a ML Engineer at Wispr, you will design features for a voice interface, optimize ML inference, and scale personalization of speech models.

The Machine Learning Engineer will develop secure AI capabilities, collaborate with teams, and build ML systems while evaluating technologies for government sponsors.

Seeking a mid-level AI/ML Engineer to design, develop, and deploy machine learning models, manage MLOps practices, and integrate AI into applications for a DoD client. Requires 3-5 years of experience and TS/SCI clearance.

The Full Stack Developer will implement OpenAI and GPT-3 tools to develop processes for creating content and editing media for podcasts.

As a Staff Machine Learning Engineer at Twilio, you will build and maintain data pipelines and ML infrastructure, ensuring high-quality data and model performance while collaborating with product and security teams.

Develop and fine-tune vision-centric large language models and visual encoders, build evaluation benchmarks, collect and prepare visual training data (including for RLHF), prototype architectures for production VLMs, publish research, and mentor junior researchers within a small applied ML research team.

Develop, iterate, and deploy machine learning models and agent systems for investment decision-making. Design experiments, build reproducible feature-generation and evaluation workflows, and work with large-scale financial and alternative datasets to identify predictive signals and improve production performance.

Optimize inference performance of large multimodal foundation models across cloud and on-robot targets. Diagnose bottlenecks, apply quantization/pruning/distillation, tune kernels (CUDA/Triton), build benchmarking and regression detection, and translate research models into deployment-ready implementations.

The Senior Python Engineer designs, develops, and deploys machine learning solutions, ensuring scalability and integration into production systems, while collaborating with engineering and data teams.

Design, build, test, deploy, and operate end-to-end AI/ML pipelines for mission systems. Automate workflows, ensure monitoring and observability, integrate models into production across hybrid cloud/on-prem environments, and collaborate with cross-functional teams while meeting security and infrastructure requirements.

Design and operate ML training and serving infrastructure, build a Kubernetes+Ray backend, improve developer experience and observability, mentor engineers, and ensure reliable, secure deployments for low-latency, high-performance systems.

Build and harden low-latency, high-throughput ML platform infrastructure. Design storage, indexing, streaming, backpressure, failure handling, and observability. Scale decisioning, improve developer experience for data scientists, manage Kubernetes deployments, and collaborate with SRE, data infra, and engineering to ensure reliable, debuggable production systems.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.