Top Machine Learning Engineer Jobs

Seeking a Machine Learning Engineer to build models from idea to delivery, maintain production models, and develop system architecture while collaborating on data-driven strategies.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Design, implement, and deploy machine learning algorithms and scalable ML solutions; collaborate with data scientists, engineers, and federal customers to build analytics capabilities and features; support application deployment and customer enablement.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

Design and optimize large-scale continual pre-training pipelines for vision-language foundation models; research novel pre-training techniques integrating multimodal sensor data; develop evaluation metrics for model performance and transferability in autonomous driving; and productionize advances from LLM/VLM research.

The role involves implementing dynamic pricing models, developing ML algorithms, and maintaining infrastructure for pricing analysis and experimentation.

Responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, and deployment on embedded devices, while working closely with cross-functional teams.

As a Machine Learning Engineer Intern, you'll develop neural decoders for Brain-Computer Interfaces and collaborate with cross-functional teams on new functionalities.

Lead the architecture, development, modernization, performance optimization, and reliability of enterprise-scale machine learning platform software. Mentor engineers, influence cross-team technical decisions, conduct code reviews, evaluate AI-assisted code, design cloud-native systems, and drive high-quality delivery across complex application layers. The role requires expertise in distributed systems, cloud infrastructure, production services, and enterprise engineering practices.

Design and build production LLM infrastructure, including orchestration, prompting, workflow layers, retrieval integrations, MCP tools, evaluation systems, dashboards, and observability. Improve platform reliability through caching, incident response, scalability, and model rollbacks. Establish architecture standards, conduct design reviews, mentor engineers, and communicate AI concepts to nontechnical stakeholders.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

Lead the development, fine-tuning, deployment, and optimization of machine learning solutions involving LLMs, vision models, and NLP for healthcare applications. Build production-grade models and pipelines, support scalable GPU and TPU training, validate performance and compliance, collaborate with healthcare and product teams, and mentor junior engineers. The role also requires documenting technical approaches and applying MLOps and LLMOps practices in regulated environments.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Build large-scale user-signal feature pipelines and production machine learning models for advertising targeting, bidding, and ranking. Own the full lifecycle from data preparation and feature engineering through training, deployment, monitoring, online serving, and A/B testing. Develop identity prediction, embeddings, intent, and conversion models; integrate outputs into real-time ads systems; improve signal quality; and provide technical leadership across Ads, Data, and Infrastructure teams.

Responsible for designing and implementing reinforcement learning algorithms, conducting experiments, and optimizing models for robotic applications, collaborating with multiple teams.

Build, deploy, and operate production ML systems (recommendation, ranking, personalization, optimization). Own feature engineering, training pipelines, online inference, experimentation, monitoring, retraining, and MLOps. Partner cross-functionally, mentor engineers, and deliver measurable business outcomes on a large-scale data platform.

Product-oriented senior ML role to build, deploy, and operate models and ML systems (classification, extraction, entity resolution, clustering, ranking, anomaly detection, forecasting, LLM pipelines). Define datasets, labeling, evaluations, monitoring, and collaborate with engineering and product to ship production-quality training/inference pipelines and model-serving infrastructure.

Lead design, development, and deployment of scalable generative AI/LLM agent solutions. Collaborate cross-functionally, integrate with AI platform, choose tools, ensure reliability, mentor engineers, and drive roadmap and stakeholder alignment.

Build and train transformer models for plant intelligence, owning data curation, architecture, training strategies, evaluation, and production deployment. Develop robust model lifecycle systems and training and evaluation infrastructure for rapid iteration. Drive machine learning projects end to end, from data collection through production, while working independently on a small team. Apply strong software engineering and scientific skills to maintain practical ML systems.

Develop high-performance, large-scale machine learning services for a programmatic advertising platform. Build and deploy AI models and data-driven algorithms for real-time ad scoring, ranking, and bid optimization. Work with data scientists and cross-functional teams on scalable data analysis, model development, production rollouts, troubleshooting, and continuous optimization using agile methods.

Research and develop machine learning systems, algorithms, prototypes, and data-fusion solutions for complex sponsor problems. Responsibilities include data selection and modeling, statistical analysis, anomaly detection, visualization, software integration, technical reporting, collaboration with sponsors, and supervision of junior staff. The role supports classified defense and intelligence applications and is performed onsite in State College, Pennsylvania, or Reston, Virginia.

Own the technical architecture and strategic direction of Disney’s News & Entertainment machine learning platform. Lead personalization, recommendation systems, ML infrastructure, data pipelines, model training, online serving, reliability, incident response, and platform standards across major media brands. Drive metrics-based business outcomes, cross-organizational engineering initiatives, technology adoption, architecture reviews, and mentorship of senior engineers.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

Develop autonomy software for mining vehicles, including perception, SLAM, motion planning, control, sensor fusion, localization, and mapping. Integrate LiDAR, cameras, radar, IMU, and GNSS while building embedded real-time systems. Create simulation, logging, and data pipelines, and validate autonomy software through bench, rig, and field testing. Collaborate with hardware, controls, and systems engineers to deliver safe, reliable vehicle autonomy.