Top Machine Learning Engineer Jobs

The role involves developing, training, and deploying machine learning models, building data pipelines, and collaborating with teams for integration and performance optimization. It supports the ML lifecycle from data collection to monitoring.

The ML Engineer will build and deploy machine learning models to optimize operations in manufacturing, including demand intelligence and production monitoring.

The role involves designing, developing, and deploying machine learning models for healthcare solutions, optimizing data processing pipelines, and collaborating with cross-functional teams to enhance patient care.

Lead the design of advanced AI systems for healthcare, develop evaluation frameworks, and drive LLM development while shaping AI strategy in a collaborative environment.

As a Machine Learning Engineer, you will design, build, and deploy ML models for underwriting at Stripe Capital, ensuring performance while collaborating with multiple teams.

The role involves training and deploying ASR models, improving pronunciation feedback, measuring performance, and expanding ASR systems for language learning.

As a Machine Learning Engineer at Verneek, you'll develop AI/NLP models, tackle complex AI research problems, and innovate within an early-stage deep-tech startup environment.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

Build and deploy LLM-based chatbots and machine learning models, integrate agents with enterprise databases and APIs, perform data preprocessing and model evaluation, automate data pipelines, and collaborate with internal teams and clients.

Lead design and deployment of generative world models and sensor simulations to produce high-fidelity synthetic datasets for large-scale simulation. Integrate multimodal and diffusion models, advance sim-to-real transfer and domain adaptation, apply VLMs and world-modeling for controllable 4D environments, and establish AI infrastructure and testing frameworks to validate edge cases for autonomous flight systems.

As a Machine Learning Engineer, you will focus on user-behavioral modeling, collaborate with teams, and engage in data processing and model deployment.

As a Machine Learning Engineer at Yobi, you will design and optimize systems for real-time model serving and inference, ensuring performance and reliability.

Develop and improve machine learning models and systems for clinical outcomes. Lead ML initiatives, mentor teams, and establish best practices in AI. Collaborate across engineering, product, and design for data-driven solutions.

Design, train, validate, and deploy ML models and production inference pipelines (CV, NLP, geospatial). Build MLOps workflows, scalable APIs, and AWS-deployed AI workloads. Monitor model drift, optimize performance, collaborate with data scientists, architects, and stakeholders, and document processes and integrations.

As a Machine Learning Engineer, you'll develop and enhance driving models, lead projects on personalized driving, and collaborate across teams to ensure system performance for automated driving.

Lead the design and development of machine learning solutions, overseeing the entire software development lifecycle and mentoring engineers. Drive innovation in Generative AI and high-performance systems.

The role involves developing and maintaining behavioral models for road users to enhance autonomous vehicle decision-making, collaborating with other teams and optimizing outputs for safety and effectiveness.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

As an AI/ML Engineer, you'll implement machine learning techniques, build AI models, and support national security applications, collaborating with various teams.

Design, build, and deploy production-grade AI/ML systems (NLP/LLMs, computer vision, generative AI). Create scalable data pipelines, microservices/APIs, and cloud- or edge-deployed models. Ensure security, observability, and performance; perform model optimization, versioning, A/B testing, and documentation. Collaborate with engineers, mission SMEs, and product teams to meet operational requirements.

Design and lead development of ML and AI solutions: explore, clean, analyze structured and unstructured data, build predictive models, apply NLP/text-mining techniques, integrate enterprise systems, visualize results, and advise clients while guiding teammates.

Lead a Labs team as a hands-on engineer building hybrid AI/optimization systems: design ML-augmented optimization, prototype and productionize solutions, architect MLOps/CI-CD, benchmark solvers, mentor engineers, and collaborate with researchers and stakeholders to scale reusable components and APIs across Kroger.

Lead architecture and implementation of Vumedi's AI discovery platform: design personalized recommendation systems, semantic search, retrieval and ranking pipelines, vector/embedding-based search, RAG/knowledge-graph integrations, scalable inference and retrieval services, and evaluate emerging AI technologies while partnering with product, data science, and engineering to deliver production LLM-powered discovery for physicians.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.