Top Remote Machine Learning Engineer Jobs

Lead design and deployment of production-grade ML systems (ranking, retrieval, LLMs). Drive architecture decisions, run experiments and evaluations, build scalable feature pipelines and monitoring, mentor engineers, and collaborate cross-functionally to integrate AI into products and scale ML capabilities.

Lead the technical vision and development of scalable ML and AI evaluation systems for Atlassian’s Rovo Chat. Build agentic capabilities, datasets, simulations, automated judges, experimentation platforms, and quality signals. Identify failure modes, ship interventions, and improve customer outcomes while balancing quality, latency, reliability, safety, and cost. Provide cross-organizational technical leadership, mentorship, design guidance, and strategic direction for AI products.

The Senior Applied Scientist will design and optimize credit risk models using machine learning, collaborating with cross-functional teams to translate business problems into technical solutions and drive scalable risk decisions.

Build and deploy production AI/ML features across the model lifecycle, including GenAI systems, RAG pipelines, data pipelines, evaluation infrastructure, and MLOps. Apply machine learning to manufacturing, supply chain, computer vision, predictive maintenance, forecasting, inventory, and logistics problems. Collaborate with engineering, manufacturing, and operations teams to deliver measurable outcomes.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Build and scale customer-facing generative AI applications for Atlassian Rovo Chat. Develop agent harnesses that improve quality, reliability, and latency; apply advanced machine learning innovations; lead complex projects from technical design through launch; mentor senior engineers; and collaborate cross-functionally with internal teams and customers.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Own the architecture and long-term evolution of hybrid, multi-tenant AI compute platforms across bare-metal OpenShift and public-cloud services. Define distributed training networking, GPU utilization and cost models, GitOps governance, model-serving standards, workload placement, identity and security controls, SLOs, disaster recovery, and platform upgrade strategies. Partner with AI, privacy, and security teams to deliver HIPAA-compliant infrastructure for training and inference workloads.

Develops full-stack digital products with integrated AI/ML capabilities. Responsibilities include designing responsive front ends, backend services, APIs, microservices, data and application pipelines, cloud-native platforms, and model inference workflows. Partners with engineering, data science, design, and product teams on roadmaps, feature engineering, validation, deployment, Agile delivery, and modernization. Applies DevOps, containerization, CI/CD, and cloud technologies while communicating technical decisions to stakeholders and supporting scalable, customer-focused solutions.

Leads a three-person computer vision team developing, training, optimizing, and deploying custom YOLO object-detection models for live full-motion video. Architects secure data and training pipelines, establishes annotation standards, manages dataset challenges, and optimizes inference using GPU acceleration. Integrates computer vision systems with video and software platforms while coordinating Agile delivery, technical direction, metrics, and code standards. The role requires advanced Python, deep learning, video processing, model deployment, and team leadership experience.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

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, 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.

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.

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.

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.

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.

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.

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 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.