Top Machine Learning Engineer Jobs

Lead development and optimization of LLMs and Mixture-of-Experts models, drive research for performance and efficiency (quantization, on-device inference, RAG, agents), integrate models into product, mentor engineers, and deliver cross-functional initiatives.

Develop and implement state-of-the-art synthetic sensor simulation methods (photorealistic images, lidar, radar). Train and evaluate ML models for synthetic data, analyze realism and utility, answer sensor-autonomy questions, and collaborate across autonomy, infrastructure, and systems teams. Role scoped as Senior/Staff IC with potential to grow into Tech Lead.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

As a Machine Learning Engineer, you'll build and enhance features for a search stack, design ML/NLP solutions, and implement an experimentation framework while ensuring code quality.

Build and deploy agentic, full-stack AI systems using LLMs, embeddings, RAG, and agent orchestration for defense and intelligence customers. Collaborate with customers, operate in classified/air-gapped environments, codify reusable patterns, and support end-to-end delivery from prototype to production.

As a Machine Learning Engineer, design imaging algorithms, build acoustic simulations, and develop computer vision systems to enhance brain imaging technology.

The role involves training and fine-tuning vision and language models, building evaluation and learning pipelines, and optimizing model performance.

The Machine Learning Engineer will design and build scalable ML data pipelines, manipulate 3D data, collaborate with customers, and ensure reliable deployment of ML systems.

The Senior Staff Machine Learning Engineer will improve Coupang's search and discovery systems using advanced algorithms, addressing bottlenecks, proposing solutions, and leading projects involving personalization and recommendations.

As a ML Engineer at Apiphany, you will develop advanced machine learning models, focusing on engineering data, AI systems, and real-world performance.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

The Machine Learning Engineer will conduct data analysis, develop ML applications, manage data transformations, and support API and ETL implementations.

The Staff Machine Learning Engineer will design and implement ML systems, build scalable architectures, collaborate with data teams, and drive technical strategy for AI applications.

The Staff Data Architect will design complex data solutions, lead data architecture initiatives, and oversee data governance while collaborating with various teams to meet business objectives.

The Staff Machine Learning Engineer will optimize the inference layer for voice applications, focusing on model serving and real-time audio processing. Responsibilities include driving performance architecture, collaborating with model partners, and developing evaluation frameworks for STT and TTS models.

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.

Build and operate production machine learning systems focused on multilingual NLP, including entity linking, classification, event extraction, deduplication, and narrative analysis. Develop GPU-accelerated workflows, fine-tune transformer models, and create LLM features such as RAG pipelines and agents on AWS Bedrock. Establish evaluation frameworks, integrate ML services into Django and event-driven platforms, lead technical architecture discussions, and mentor team members.

Productionize and optimize ML systems for AI reasoning over structured data. Responsibilities include improving training and inference performance, porting workloads from CPU to GPU, building training and deployment infrastructure, developing experimentation and observability tools, and implementing new model architectures and fine-tuning methods using Python and PyTorch. The role requires strong ML research, systems engineering, software architecture, optimization, and experimental design skills.

Lead the architecture and hands-on delivery of production AI systems using agentic frameworks, RAG, LLM orchestration, document intelligence, knowledge graphs, and evaluation pipelines. Manage and mentor 4–8 engineers, direct architecture and delivery, build Python and Java services, establish observability and CI/CD, and integrate AI platforms with enterprise systems. The role owns technical strategy for multi-agent workflows, vector retrieval, prompt engineering, validation, and AI-assisted developer tooling.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Build and improve production ML and LLM-powered systems to raise assistant and agent quality. Design evaluation, benchmarking, and monitoring; develop signals, prompts, workflows, and model-driven logic; work on RAG, retrieval, personalization, RL, and orchestration; partner with product and engineering to ship production systems and support data/ML infrastructure for experimentation and continuous improvement.

Design, implement, and evaluate ML and signal/image processing algorithms for geospatial/intelligence sensor data. Develop data ingestion, visualization, and scalable software tools, collaborate with teammates and customers, document results, and support deployment on customer systems. Apply statistical techniques to assess algorithm performance.

Design, train, and deploy ML models and pipelines to optimize Databricks serverless compute. Build end-to-end ML4Sys solutions, scale infrastructure, implement optimization and scheduling algorithms, and collaborate on product roadmap to improve performance and cost efficiency.