Top Remote Machine Learning Engineer Jobs in Seattle, WA

Lead end-to-end machine learning initiatives for Block’s conversational support systems. Responsibilities include developing and maintaining chatbot and recommendation models, researching LLM, RAG, fine-tuning, and real-time inference architectures, shaping long-term ML roadmaps, and guiding cross-functional teams. The role also requires communicating technical strategy and outcomes to leadership and external stakeholders while delivering scalable, production-ready AI solutions across Cash App, Square, and other Block products.

Leads end-to-end machine learning initiatives for Block’s support experiences, including conversational AI, chatbots, recommendation systems, and agent tools. Defines ML roadmaps, guides research into LLMs, RAG, fine-tuning, and real-time inference, and designs, deploys, and maintains production models. The role requires strategic technical leadership, cross-functional influence, stakeholder communication, and extensive experience delivering applied ML and generative AI systems.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.

The Principal Machine Learning Engineer role focuses on developing and leading machine learning engineering initiatives at Atlassian. The provided description emphasizes Atlassian’s flexible work model, compensation, benefits, inclusion commitments, and employment verification requirements, but does not provide detailed technical responsibilities or qualifications.

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

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.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

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.

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.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

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.

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.

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 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.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

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 production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

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.

Senior AI/ML Engineer responsible for translating federal supply chain and logistics problems into analytical solutions, developing forecasting, classification, and NLP models, and deploying them into production. The role owns model development, data pipelines, monitoring, retraining, drift detection, validation, documentation, and stakeholder communication. Work includes operating within government-controlled environments and meeting federal bias evaluation and documentation requirements.