Top Machine Learning Engineer Jobs

Develops and deploys generative AI capabilities for defense information operations, including LLM applications, agentic workflows, RAG systems, prompt engineering, model evaluation, and API integrations. The role requires translating operational needs into deployable AI solutions, implementing responsible AI safeguards, supporting demonstrations and milestones, and researching multimodal and synthetic-data techniques. Candidates need Python, cloud, MLOps, vector database, and defense-platform experience, plus U.S. citizenship and eligibility for a TS/SCI with Polygraph clearance.

Architect and build a multi-cloud ML training platform for decentralized large-model training and inference across consumer devices and cloud instances. Responsibilities include infrastructure orchestration, distributed compute, GPU workloads, checkpointing, data streaming, network-condition simulation, node-churn management, observability, reliability, and resilient job execution. The role requires strong Python, infrastructure-as-code, Kubernetes, distributed systems, decentralized networking, and SRE experience.

Design, develop, and deploy production AI/ML systems involving foundation models, agentic workflows, RAG, multimodal learning, model evaluation, and edge inference. Build data pipelines, cloud architectures, APIs, and software integrations while applying AI assurance practices. Lead technical tasks, mentor junior staff, collaborate with customers and cross-functional teams, contribute to Agile delivery, and support advanced government and commercial R&D proposals.

Supports the development, testing, documentation, and production delivery of AI and machine learning solutions, including generative AI and agent-based applications. Works with data and application engineers to build AI workflows, evaluate outputs, address data quality concerns, follow responsible AI practices, and maintain code quality. The role involves client collaboration with guidance, participation in reviews, and continued development of AI, machine learning, data engineering, and production delivery skills.

Develop production machine learning systems for Glean’s Enterprise Brain, focusing on user understanding, task prediction, LLM workflows, personalization, reinforcement learning, ranking, and agentic systems. Build scalable evaluation and benchmarking loops, maintain ML pipelines and knowledge graphs, improve data and model quality, and collaborate cross-functionally to deliver reliable enterprise AI solutions.

Designs and operates evaluation systems for evolving machine learning models. Responsibilities include defining metrics, building data and automated judging pipelines, analyzing model failures, simulating users and environments, running experiments, and documenting findings. The role requires production-quality Python, PyTorch, reinforcement learning evaluation or simulation experience, and strong machine learning research or open-source contributions.

Lead the technical strategy, architecture, and delivery of large-scale machine learning systems for autonomous driving. Develop end-to-end AI methods, define dataset and auto-labeling requirements, mentor senior engineers, influence multiple teams, and integrate autonomy technology across Uber. The role requires expertise in Python, Linux, PyTorch, autonomous systems, and scalable ML infrastructure, with preferred experience in C++, CUDA, ROS, foundation models, and high-performance optimization.

Develop and evaluate machine learning and LLM-based systems, including neural network tuning, model development, benchmarking, experiment design, and performance validation. Use Python or R, deep learning frameworks, SQL, GPUs, and version control systems. Preferred work includes Hugging Face, cloud architecture, semantic search, Tableau, Explainable AI, ONNX, and web development. Requires U.S. citizenship and an active TS/SCI with Polygraph clearance.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

The job description content is missing. Based on the title, this role involves senior-level machine learning engineering. Atlassian offers flexible work arrangements, location-based compensation, benefits, bonuses, commissions, and equity.

Designs, builds, and operates scalable machine learning platform capabilities supporting feature engineering, model training, inference, monitoring, and lifecycle management. The role delivers reliable software, improves platform performance and observability, leads architecture decisions, and partners with engineering stakeholders to expand shared ML infrastructure. It also applies AI/ML concepts and AI-assisted development practices to build secure, maintainable, and efficient production systems.

Designs, builds, and operates scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. The role delivers reliable software and infrastructure, improves platform performance and observability, influences architecture and technical standards, and partners with engineering stakeholders to safely integrate AI/ML solutions at scale.

Develop, optimize, and deploy production-grade computer vision and deep learning models for real-time perception and navigation on edge hardware. Integrate AI systems with robots, drones, unmanned systems, and swarms while optimizing inference with CUDA and TensorRT. Manage containerized deployments using Docker and Kubernetes, contribute to CI/CD and automated testing, and transition research prototypes into reliable production features.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Leads the design, development, deployment, and maintenance of production AI/ML solutions in classified AWS environments. Builds RAG and agentic AI systems, provides architectural guidance, supports secure system integration, and communicates technical findings to technical and nontechnical stakeholders. The role also includes data architecture, model optimization, containerized application development, DevSecOps practices, and technical leadership for AI/ML engineering teams.

Design, train, deploy, and optimize computer vision models for automated vehicle damage detection. Build high-throughput containerized microservices using REST/gRPC, maintain Kafka data pipelines, and own the full machine learning lifecycle from experimentation through production optimization. Collaborate with stakeholders to develop scalable, reliable inspection solutions and lead technical design efforts.

Designs and trains computer vision models for vehicle damage detection, develops high-throughput model-serving microservices, and builds Kafka-based data pipelines. Owns the machine learning lifecycle from experimentation through production deployment and optimization. Collaborates with stakeholders to deliver scalable, reliable inspection solutions, while improving inference latency, accuracy, and robustness in high-traffic environments.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Leads the design, development, deployment, and operationalization of enterprise AI/ML, generative AI, and automation solutions. Provides technical leadership across architecture, engineering practices, evaluation, monitoring, governance, security, and responsible AI. Partners with cross-functional stakeholders, mentors engineers, establishes reusable standards, evaluates production readiness, and ensures scalable, reliable, compliant, and cost-effective delivery of AI/ML capabilities.

Build scalable data and machine learning pipelines for cloud-based model training and deployment. Develop, fine-tune, and productionize traditional, deep learning, generative AI, multimodal, and agentic systems. Implement feature engineering, event-driven ETL/ELT, model monitoring, CI/CD, RAG, prompt engineering, and REST APIs. Optimize model performance and compute costs while collaborating with Product and Engineering teams on enterprise Generative AI adoption and roadmap development, including applications for semiconductor manufacturing data.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

Build and operate enterprise AI integration products, including MCP servers, agent-facing APIs, React and TypeScript experiences, semantic retrieval systems, and automated AI quality evaluations. Improve latency, reliability, token efficiency, observability, and security across distributed systems. Lead cross-functional engineering initiatives, mentor engineers, and implement enterprise safeguards such as tenant isolation, audit logging, OAuth, and prompt-injection defenses.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.