Top Senior Machine Learning Jobs in Seattle, WA

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 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.

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.

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.

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.

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.

Leads a team developing AI capabilities for CrowdStrike’s cross-platform endpoint sensor. Owns the AI-on-endpoint roadmap, including machine learning model operationalization, telemetry data pipelines, on-device inference, architecture, feature delivery, and cross-functional collaboration. Manages hiring, mentoring, engineering quality, testing, performance, and releases across Windows, macOS, and Linux sensor platforms.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Leads machine learning and software engineering teams building models, agentic systems, data pipelines, and ML infrastructure for advertising integrity and recommendations. Owns technical strategy, roadmap, model quality, reliability, privacy, and responsible AI. Partners cross-functionally to deliver measurable outcomes, scales LLM-powered ad review, contributes technically, and hires and mentors high-performing engineers.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

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.

Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.

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.

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.

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.

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.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Convert research innovations into reliable product capabilities, develop reusable infrastructure, resolve performance and scalability challenges, contribute to architecture, and coordinate complex technical projects. The role also emphasizes security, privacy, AI safety, Responsible AI, cross-functional collaboration, and mentoring engineers.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Develop reusable infrastructure and services, integrate research innovations into reliable products, drive architecture, and solve scalability, performance, reliability, security, privacy, and Responsible AI challenges. Provide hands-on technical leadership and mentorship across complex engineering initiatives.

Leads the engineering team responsible for Nordstrom’s personalization engine, including recommendation models, training pipelines, real-time serving, and complete-look experiences. Owns technical direction, delivery, reliability, model and data quality, incident response, and team development. Partners with product, data science, platform engineering, and business stakeholders while remaining hands-on with architecture, code reviews, and AI-native development practices.

Leads the design, experimentation, productionization, and optimization of machine learning, deep learning, generative AI, LLM, and agentic systems. Develops Python-based proofs of concept and production solutions, evaluates and tunes models, integrates generative AI into ML platforms, improves system performance, and partners with product and engineering teams. Acts as an ML subject matter expert while mentoring practitioners and influencing product and technical decisions.

Leads technical vision for ads conversion machine learning, developing low-latency deep neural networks and modeling user intent from text, visual, and behavioral signals. The role includes mentoring engineers, guiding applied ML projects, collaborating on ad products, using AI tools to accelerate development, and applying statistical methods and A/B testing to improve model quality and iteration speed.

Lead the technical strategy and development of machine learning solutions for Uber’s homepage personalization, including sequential recommendation, intent-aware ranking, LLM-based systems, and agentic AI. Partner with product teams to improve engagement and business outcomes, influence technical direction across teams, and mentor machine learning engineers.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.