Top Machine Learning Engineer Jobs

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

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.

Lead and build an ML Acceleration team to define strategy and implement scalable solutions to speed ML training. Hands-on role combining technical design (distributed training, hardware and model optimization) with people management, cross-functional collaboration, impact measurement, and continuous performance improvements for production autonomous vehicle ML systems.

Lead development of real-time machine learning and computer vision algorithms for ultrasound imaging in Intuitive’s robotic-assisted biopsy system. Responsibilities include detection, segmentation, classification, temporal tracking, artifact mitigation, image fusion, model optimization, and deployment into high-performance C++ medical-device software. The role also supports system integration, testing, formal design documentation, and patent applications while collaborating with clinical and engineering teams under Good Machine Learning Practice principles.

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.

Customer-facing AI/ML Solution Engineer supporting strategic accounts through technical presentations, demonstrations, workshops, solution architecture, proof-of-concepts, MVPs, and customized prototypes. Partners with account and regional solution engineering teams to accelerate sales, drive AI/ML adoption, translate customer needs for product and engineering teams, and enable broader technical proficiency. The role also documents best practices and requires 30–40% travel.

Develops AI and machine learning software solutions that improve organizational efficiency and decision-making. Responsibilities include building applications in Python, Java, or C++, working with NLP, deep learning, or computer vision, deploying models to production, and supporting Linux or UNIX systems. Preferred experience includes cloud-native and containerized development, infrastructure as code, major cloud platforms, hybrid architectures, and defense or intelligence environments.

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.

Designs, builds, documents, tests, and maintains large-scale data pipelines across platforms and sources. Performs ETL and ELT while ensuring data governance, security, quality, validation, anomaly detection, and compliance. Collaborates with architects, analysts, and stakeholders to translate business needs into technical solutions, develops data models, and mentors junior data engineers.

Build and operate large-scale data systems supporting AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, dataset versioning, reproducibility, and high-throughput delivery across diverse modalities. The role requires operating petabyte-scale storage and pipeline systems, applying distributed systems and data modeling expertise, and maintaining strong software engineering practices including testing, CI/CD, and code review. Collaboration with cross-functional teams is also required.

Lead and manage a new ML Acceleration team, driving strategy and development for faster ML model training and optimizing performance.

Design, develop, and deploy production-grade full-stack AI applications and agentic AI solutions. Build AI agents, multi-agent workflows, RAG pipelines, LLM applications, conversational interfaces, and scalable microservices using Java or Python and AWS services. Implement prompt engineering, evaluation, observability, guardrails, CI/CD, containerization, APIs, and infrastructure as code. Troubleshoot and optimize production systems in enterprise environments.

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 large-scale machine learning models powering predictive aviation products, including runway landing, gate arrival, and taxi duration predictions. Work with hundreds of millions of historical data examples and complex data types, optimizing model performance and efficiency. Collaborate within an agile team, contribute original ideas, and develop solutions for open-ended aviation intelligence challenges.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Architect and own the ML roadmap for continental-scale grid intelligence. Design and deploy multimodal ML, GNNs, physics-informed models, and RL for grid planning, simulation, and operations. Mentor senior engineers, set production standards, and collaborate with power-systems scientists and utility partners to deliver scalable, reliable ML systems.

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.

You will build a search and assistant product, improve personalization, train models, and write robust code while mentoring junior engineers.

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, build, and deploy end-to-end machine learning solutions for growth marketing platforms. Develop scalable model training, validation, and serving pipelines, lead experiments, present results to leadership, and mentor junior engineers to improve customer acquisition, retention, and ROAS.

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.

Lead workload-driven AI/ML systems architecture across hardware and software. Own workload characterization, analytical performance modeling, KPI definition, and hardware performance analysis to guide SoC, memory, and HW/SW co-design decisions. Evaluate compute, memory, latency, and software bottlenecks; collaborate with CPU, compiler, runtime, and ML framework teams; present recommendations to leadership; and mentor junior engineers. The role requires expertise in processor and memory architecture, edge AI acceleration, performance modeling, and cross-functional technical leadership.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Own cloud-native MLOps infrastructure for multimodal sensor data ingestion, processing, labeling, validation, model traceability, deployment gates, monitoring, drift detection, and diagnostics. Partner with data scientists, ML engineers, and stakeholders to ensure reliable pipelines, fresh features, robust model performance, and effective post-deployment metrics. Build visualization and support tools that help technical and nontechnical users understand perception and recommendation systems.