Top Machine Learning Engineer Jobs

The job involves developing machine learning algorithms, designing data pipelines, utilizing cloud infrastructure for ML solutions, and ensuring model governance practices.

The Senior Machine Learning Engineer will develop and enhance ML systems for content resolution and work on multimodal understanding involving music, video, and metadata. Responsibilities include leading ML pipeline evolution, experimentation for quality improvements, and mentoring team members.

Develop Cloud Data Infrastructure for Assured AI in autonomous vehicles, focusing on ML model training, evaluation, and deployment.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

Lead software engineer responsible for designing high-level and detailed architectures, performance tuning, and building distributed systems. Work with LLM/ML and big-data technologies (e.g., Langchain, Spark), develop in Python and C++, and design data solutions with strong SQL and database fundamentals. Collaborate across teams and integrate diverse frameworks into practical solutions.

The role involves designing and implementing agentic workflows for ML, building AI-native systems, and improving model development through automation and self-improving loops.

Build and maintain production ML and computer vision systems for automated battery sorting: image acquisition, inference, sensor integration, model training/deployment, observability, CI/CD, and cross-functional collaboration to improve on-floor performance.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

Manage the ML lifecycle, collaborate with teams, optimize production code, evolve solutions, and stay updated with ML developments.

The role involves optimizing machine learning models for autonomous driving by implementing performance techniques and collaborating with cross-functional teams on architectural decisions.

Design, develop, and deploy production-grade AI/ML models and scalable architectures. Lead model training, validation, deployment, monitoring, and cross-functional integration. Conduct research, document methods, and share knowledge to drive business impact and operational reliability.

As a Senior Machine Learning Engineer, you'll design, build, and deploy ML models, improve algorithms, mentor juniors, and ensure code quality.

The Senior Machine Learning Engineer will design, implement, and deploy ML models, contribute to engineering standards, and mentor junior engineers.

Nagarro is looking for an Associate Staff Engineer specializing in Machine Learning to tackle client challenges and enhance team collaboration, while pushing solutions further.

Senior individual-contributor building production agentic AI and RAG systems end-to-end: document ingestion/OCR, chunking, embeddings/vector indexes, retrieval orchestration, prompt engineering, evaluation harnesses, FastAPI services, database schemas, governance controls, and mentoring mid-level engineers.

Lead architecture and delivery of production-grade agentic AI and RAG systems. Own multi-agent orchestration, embedding/vectorization pipelines, retrieval, evaluation frameworks, and team leadership for engineers building document-intelligence and AI-assisted developer tooling. Responsible for production delivery, observability, CI/CD, and cross-team integration in an enterprise finance environment.

Design and deploy end-to-end machine learning solutions: gather/process large datasets, build predictive and topic-modeling systems, develop data pipelines, use AWS and NLP APIs, containerize and deploy models, lead technical teams, and manage project delivery and stakeholder communication.

Responsible for architecting high-performance machine learning silicon, collaborating with software and design engineers, and driving innovation in AI hardware solutions.

The role involves designing and deploying AI/ML models, collaborating with stakeholders, and enhancing operational efficiency through innovative solutions.

Design and build the intelligence layer: retrieval and memory systems, RAG pipelines, feedback and evaluation loops, RL and fine-tuning experiments, LLM integration, observability for AI performance, and collaborate to productize AI features.

Optimize and deploy computer vision, signal processing, and ML algorithms to GPU-enabled embedded platforms. Profile CPU/GPU performance, reduce latency, containerize solutions, work in Linux, and collaborate with ML engineers to transition models into production on low SWaP systems.

Design, prototype, and operationalize ML models and data pipelines for temporal, geospatial, and track-based mission data. Build data processing, feature engineering, containerized cloud deployments (AWS/Docker/Kubernetes), APIs, and scalable evaluation/monitoring workflows while following Agile and software engineering best practices.

Develop and deploy deep learning computer-vision systems: source and curate data, design and train models (object detection), implement production-ready PyTorch/Python software, integrate with AWS, apply CI/CD and testing, and collaborate on research and technical solutions for intelligence-focused applications.

Develop backend systems and ML pipelines for computer-vision applications using Python, Docker, Kubernetes and AWS. Build RESTful APIs and streaming services, optimize performance, follow SDLC best practices, and collaborate in an Agile team to deploy containerized deep learning models.

Own and improve recommendation and search systems across feed, discovery, and search. Build retrieval and ranking (candidate generation, embeddings, two-tower models), run experiments, create user/content/session representations, and ship production ML systems with monitoring to improve retention, engagement, and content distribution.