Top Machine Learning Engineer Jobs

The Machine Learning Engineer designs and implements data-driven solutions, develops scalable ML systems, analyzes datasets, and leads client collaborations.

Partner with sales to drive technical enterprise deals: lead discovery, design solutions, run demos and POCs, address security/compliance and integrations, build trusted technical relationships, and liaise with product/engineering to inform roadmap and support implementation and contracting.

Design and deploy machine learning models for content relevance on Patreon. Collaborate with cross-functional teams, analyze data, and iterate on models for performance improvement.

As a Senior Machine Learning Engineer, you will build and productionize models, develop scalable ML infrastructure, and collaborate with teams to optimize model performance while mentoring others.

This role involves developing recommendation systems, creating metrics for model performance, mentoring, and collaborating with cross-functional teams to enhance data-driven insights and algorithm efficiency.

The AI/ML Software Developer will design, develop, and maintain AI applications, collaborate with teams, solve problems, and manage software debugging and documentation tasks.

The role involves shaping the CV strategy, building ML solutions in production, and integrating them with software systems, focusing on computer vision applications.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

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.

The Senior Software Engineer is responsible for designing, developing and maintaining AI-powered services, enhancing ML capabilities, and leading cross-functional teams in a collaborative environment to innovate and implement best practices in document intelligence.

The AI ML Operations Engineer will design and manage machine learning systems from data collection to deployment, ensuring system reliability and optimizing workflows.

Develop end-to-end perception solutions for autonomous driving, leveraging deep learning and data-driven approaches to enhance system accuracy and efficiency.

Design, implement, and scale web and data pipeline solutions focused on generative AI and LLMs. Improve AI/ML infrastructure for model development, training, deployment, and prompt engineering. Partner with product and stakeholders to prioritize applied AI use cases, ensure PHI security and regulatory compliance, mentor junior engineers, and contribute APIs and integrations to embed generative AI into healthcare applications.

The role involves managing ML infrastructure, building scalable data pipelines, operating training frameworks, leading projects, and ensuring best DevOps practices for machine learning.

The Staff Software Engineer will lead ML infrastructure, architect systems for model training and deployment, and mentor engineers on best practices.

The role involves building data infrastructure for ML models, managing petabytes of camera data, and collaborating with cross-functional teams to enhance model quality.

The role involves building standardized training frameworks, profiling model performance, collaborating with teams, and improving related tooling.

The role involves designing and implementing MLOps pipelines, optimizing machine learning processes, and ensuring model performance and compliance in a collaborative environment.

Develop machine learning models for multi-object tracking and road feature estimation, transitioning solutions from lab to production. Collaborate on algorithm design and maintain software best practices.

The role involves developing AI/ML models to predict organoid growth and optimizing differentiation protocols through computational methods, collaborating with experimental teams.

The Automation Engineer will design, maintain automation equipment, support sites, develop solutions, and ensure timely delivery of automation requirements.

Design and implement scalable LLM- and RAG-based systems integrating knowledge graphs and multi-omics data, build agentic orchestration and conversational AI, collaborate with data engineering and experimental scientists, and deploy pipelines that synthesize biological information to support therapeutic discovery.

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

Develop and optimize 3D graphics algorithms for sensor simulation, leveraging GenAI/ML techniques. Collaborate with various teams to enhance simulation realism and generate high fidelity data.

Design and deploy production-grade ML models and pipelines for lane- and route-level semantic/topological mapping. Research and evaluate neural architectures (detection, segmentation, depth, 3D), build data curation/auto-labeling/active learning systems, develop evaluation metrics, and integrate mapping features into the AV fleet through testing, reviews, and collaboration with autonomy teams.