Top Machine Learning Engineer Jobs

Lead development of cloud-based MLOps and ML platform components supporting model training, deployment, monitoring, and serving. Build AWS infrastructure and microservices, automate testing and release workflows, monitor platform health, optimize high-volume transactional systems, and apply secure AI-assisted development practices. Collaborate within an agile team to deliver scalable, stable, and secure technology solutions.

Design, build, and operate scalable hybrid human/machine data labeling platforms for autonomous vehicles. Own end-to-end projects, define platform roadmap, integrate ML-driven annotation, build high-performance UIs/APIs, and collaborate across ML, product, and operations to improve labeling quality and velocity.

Design and build scalable, reliable ML training infrastructure for advanced AI research and model development. Optimize distributed training performance, resource utilization, data loading, and costs across heterogeneous GPU and cloud environments. Improve platform observability, debuggability, operational excellence, and user experience. Collaborate with ML engineers, research scientists, and cross-functional partners to integrate new technologies supporting intelligent driving systems.

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.

As an AI Engineer, you will develop and optimize AI systems for legal tasks, work on integrating models, and establish evaluation frameworks to enhance decision-making capabilities in complex legal workflows.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

The Principal AI/ML Engineer will lead the AV ML Infra team, overseeing scalable ML infrastructure, project ownership, collaboration, and team mentorship in AI and ML innovation at GM.

Develop ML and multimodal AI systems that integrate neurophysiological, behavioral, and sensor data for cognitive state estimation and decision support. Build algorithms and prototypes, apply cognitive science to improve explainability and human-AI collaboration, contribute to proposals/publications, and demonstrate capabilities for mission-driven R&D.

As a Staff AI/ML Engineer, you'll enhance developer productivity, mentor engineers, drive best practices, and implement scalable solutions for AV tools and services.

Design, scale, and maintain robust machine learning systems for AI-powered physical machines. Help define coding standards and the technology stack, work across software and hardware, solve ambiguous problems, and mentor new hires. Relevant work may include object recognition, reinforcement learning, and deep neural networks in a fast-growing AI and robotics startup.

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.

Build enterprise-scale machine learning and agentic search systems enabling AI agents to discover, retrieve, evaluate, and synthesize information. Work spans retrieval, ranking, LLM reranking, multi-step search, tool intelligence, grounding, citations, evaluation, and production infrastructure while addressing permissions, freshness, latency, trust, and multi-tenant requirements.

Build and operate scalable machine learning systems for Atlassian’s Growth organization, including personalized recommendations, ranking, decisioning, experimentation, and customer journey optimization. Partner with product, engineering, data science, analytics, marketing, and sales teams to translate growth opportunities into measurable production capabilities. Develop datasets, features, models, evaluation frameworks, and monitoring systems while optimizing for customer value, fairness, quality, and long-term business outcomes.

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

Build and operate scalable machine learning systems for personalized recommendations, growth journeys, cross-product expansion, and sales experiences. Design models, datasets, features, evaluation frameworks, and decisioning services; run offline policy evaluations and online experiments; and monitor attribution, latency, quality, and fairness. Partner with product, engineering, data science, analytics, marketing, and sales teams to turn ambiguous growth opportunities into measurable production capabilities and strategic ML initiatives.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

The job description does not include specific responsibilities or qualification details. The role is identified as a Senior Machine Learning Engineer at Atlassian, with flexible work location options and a U.S. base salary range varying by geographic pay zone.

This is a senior machine learning engineering role at Atlassian, but the job description contains no specific responsibilities or qualification details. The position offers flexible work arrangements where Atlassian has a legal entity and includes location-based compensation, benefits, bonuses, commissions, and potential equity.

Lead the technical direction for ML-powered search serving, including ranking, retrieval, inference pipelines, and serving infrastructure. Drive improvements in search quality, latency, cost, and reliability through experimentation and model optimization. Establish ML systems standards for evaluation, rollout safety, and observability; pursue advanced search initiatives; mentor senior engineers; and partner across search, ML platform, and product teams.

Lead the design, development, deployment, and optimization of production machine learning systems for search, recommendations, personalization, NLP, deep learning, and AI applications. Own projects across the full ML lifecycle, design scalable architectures and evaluation strategies, collaborate with product and engineering teams, translate research into reliable systems, and mentor other engineers.

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.

Lead the technical direction of GenAI products and knowledge systems. Design and scale infrastructure for training, fine-tuning, serving language models and embeddings, and build retrieval, hybrid search, and RAG pipelines. Partner with applied scientists and engineering teams to rapidly prototype, productionize, and optimize reliable ML services. Establish best practices for deployment, monitoring, evaluation, latency, throughput, and resource efficiency.

Lead DigitalOcean’s AI security strategy, governance framework, threat modeling, and multi-year roadmap. Partner with security, engineering, and data science teams to secure AI products, deploy guardrails, conduct adversarial testing and red teaming, and monitor AI/ML systems. Provide architectural reviews, executive risk briefings, and guidance on emerging threats including prompt injection, model supply-chain risks, agentic AI, and data exfiltration.

Lead DigitalOcean’s AI security strategy, governance framework, roadmap, and organizational programs. Build and operationalize AI/ML security controls, tooling, guardrails, and automation; conduct threat modeling, adversarial testing, and AI red teaming. Review AI architectures, guide high-risk product decisions, monitor deployed models, and address risks involving RAG, agents, model supply chains, and prompt injection. Represent AI security to executives and external communities while translating emerging research and threats into engineering guidance and policy.