Top Machine Learning Engineer Jobs

The AI/ML Engineer will develop AI/ML algorithms, analyze multi-domain datasets, and provide training within a research-oriented team.

As a Machine Learning Engineer Intern, you'll bridge the sim-to-real gap in data for autonomous truck software, optimizing and integrating BEV features and supporting self-play reinforcement learning.

The role involves integrating and optimizing machine learning models for autonomous vehicle scenario generation, collaborating on AI-driven safety validation, and contributing to data-driven metrics and tooling.

Design and implement high-performance APIs and microservices using Python and AWS, focusing on generative AI and scalable applications.

Join PlusAI as a Machine Learning Engineer Intern to develop AI-based features for autonomous trucks, focusing on multi-modal data processing and event classification.

The intern will prototype a generative simulator for diverse driving scenarios, evaluate existing tools, and integrate outputs into data pipelines for autonomous driving applications.

As a Machine Learning Engineer Intern, you'll modify models for ego-vehicle predictions, optimize loss functions, and evaluate trajectory diversity and accuracy.

Senior AI/ML engineer responsible for deploying machine learning algorithms with a focus on NLP and MLOps. Must hold an active Top Secret/SCI clearance with favorable polygraph and meet advanced degree or equivalent experience requirements. Role emphasizes productionizing models and supporting mission-driven defense projects.

Build and deploy production AI/LLM systems and agentic workflows for engineering productivity, diagnostics, search, and automation. Design retrieval, tool integration, evals, failure handling, and reusable skills; collaborate with infrastructure and domain teams to monitor and improve systems in production.

Lead ML strategy and integrate ML into real-time game systems and toolchains. Design and deploy LLM techniques, build training and inference pipelines for on-device and real-time models, prototype ML-driven gameplay features, analyze telemetry for personalization, and mentor engineers.

Provide technical leadership for AI/ML development under DMA/CIO7 tasks; design, develop, and deploy advanced AI/ML and agentic AI capabilities; ensure compliance with DIA QAF including model validation, bias testing, and performance optimization; coordinate with data scientists, software engineers, and mission stakeholders; recommend emerging AI/ML solutions.

As a Lead Machine Learning Engineer, you will operationalize ML models, collaborate with teams, solve complex problems, optimize data pipelines, and implement best practices in ML deployment.

As an MLOps Engineer II, you'll enhance ML workflows, support engineers, improve model deployment processes, and advocate for best practices across teams.

As an MLOps Engineer at Kensho, you will develop tools and processes for ML workflows, assist ML engineers, and drive adoption of best practices across teams.

The Senior ML/MLOps Engineer designs and manages scalable machine learning solutions on Databricks, collaborating with teams to productionize models and ensure reliable operations.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Train, fine-tune, and deploy vision-language-action and robot manipulation models across the full pipeline. Handle data curation, pre-training and fine-tuning, sim-to-real distillation, debugging, evaluation, and iterative autonomous improvement on physical robots.

Design, build, and productionize ML models and infrastructure at scale. Collaborate with product and data science teams, develop and validate models, construct data pipelines, deploy and monitor models, apply CI/CD, and ensure Responsible/Explainable AI and high availability for ML systems.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design and implement agent harnesses and post-training pipelines, build evaluation benchmarks, develop RL environments and reward models, and debug/optimize multi-GPU training runs to improve LLM capabilities for agentic applications.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

The Principal Machine Learning Engineer will lead hands-on model development, oversee predictive modeling, and drive AI initiatives, ensuring models are effective and operationally sound.

The Machine Learning Engineer designs, builds, deploys, and maintains machine learning models, optimizing their performance for business value. Responsibilities include managing ML pipelines and collaborating with data teams to implement MLOps best practices.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.