Top Machine Learning Engineer Jobs

Architect and ship large-scale ML and evaluation systems for Atlassian Rovo Chat. Define technical strategy for measuring helpfulness, correctness, reliability, and agentic capabilities; develop datasets, simulations, automated judges, experimentation platforms, and production quality signals. Identify failure modes, implement interventions, and measure customer impact. Lead cross-functional initiatives, establish reusable AI quality standards, and mentor engineers while balancing quality, safety, latency, reliability, and cost.

Build and deploy advanced machine learning and agentic AI systems that improve Airbnb’s customer service experience. Responsibilities include developing AI assistants, integrating ML products, optimizing model performance, applying LLM techniques such as fine-tuning and RAG, building multi-agent architectures, and shipping scalable production ML systems. The role collaborates closely with Engineering, Product, and Design teams and contributes to emerging AI initiatives from concept through production.

Build, deploy, and improve production machine learning systems, including models, data pipelines, AI services, and monitoring workflows. Responsibilities include fine-tuning and evaluating models, designing scalable training and inference infrastructure, optimizing performance and cost, integrating APIs and internal tools, and partnering with product, research, and design teams to deliver reliable features. The role requires hands-on software delivery experience, cloud knowledge, and practical application of machine learning or analytics-heavy systems.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Build and deploy scalable regulatory model applications and cloud modernization solutions using PySpark, Python, Databricks, and AWS. Design secure, resilient architectures; develop distributed data processing systems; write tested, documented production code; support reliability and observability; and collaborate with product, data, model, and platform teams. The role may also enable AI agent development through LLM workflows, orchestration, evaluation, and guardrails.

Design, prototype, and productionize LLM-based architectures for structured and unstructured financial data. Implement pre-training objectives, optimize training throughput, monitor and iterate on deployed models, mentor engineers, and collaborate with product teams to improve data quality, reduce resolution times, and increase consistency across operational workflows.

Build and support scalable, production-grade regulatory model applications and cloud modernization solutions. Design distributed data processing systems using PySpark, Python, and Databricks; implement secure and resilient AWS architectures; write tested, documented code; and contribute to architecture reviews and release readiness. Collaborate with product, data, model, and platform teams. Experience enabling AI agent development, including LLM workflows, orchestration, evaluation, and guardrails, is a plus.

Build and lead large-scale, real-time machine learning systems for Pinterest’s growth content and notification ranking. Drive model improvements through A/B testing, own training and serving infrastructure, establish evaluation and monitoring practices, and guide technical direction across teams. Mentor engineers, conduct rigorous reviews, collaborate cross-functionally, apply state-of-the-art research, and improve engineering quality, system reliability, latency, and cost efficiency.

Develop and deploy production-scale machine learning models, from data gathering and neural network design through training, optimization, scalable deployment, and continuous performance improvement. Build highly available distributed systems and backend APIs, improve engineering standards and tooling, conduct metric-driven research, mentor engineers, collaborate cross-functionally, and help define product strategy and roadmaps.

Develop and productionize deep learning models across the full machine learning lifecycle, including neural network design, data preparation, training, tuning, deployment, monitoring, and continuous improvement. Build scalable data pipelines for large datasets, collaborate with Backend, DevOps, and data-labeling teams, and apply security and information-protection practices. The role requires strong Python and machine learning framework expertise, production deployment experience, and familiarity with distributed data technologies.

Develop and deploy deep learning models into production at scale. Responsibilities include designing neural networks, collecting and refining data, training and tuning models, building scalable data pipelines, deploying high-throughput systems, analyzing results, improving model performance, and collaborating with Backend, DevOps, and data-labeling teams. The role also includes applying secure coding practices and protecting information assets.

Leads end-to-end machine learning development, including neural network design, data collection, model training, tuning, deployment, monitoring, and optimization at production scale. Builds scalable code and data pipelines, develops novel ML algorithms, improves backend systems and engineering processes, conducts metric-driven research, mentors engineers, and contributes to product strategy and roadmap planning.

Design, deploy, and integrate machine learning models into digital-twin and multiphysics simulation environments for laser-driven fusion systems. Build physics-informed models, surrogate and reduced-order models, ML pipelines, anomaly detection, uncertainty quantification, Bayesian optimization, and reinforcement-learning workflows. Collaborate with physicists and engineers to accelerate simulations, optimize system performance, support predictive diagnostics, and establish reproducible engineering practices.

Build and deploy machine learning systems for ransomware detection, suspicious sessions, user behavior analytics, and anomaly detection. Own high-volume ML pipelines using Spark, GCP Dataflow, and Vertex AI; develop feature stores; operate production models; and collaborate with platform, engineering, and product teams. The role includes on-call support and requires strong Python, cloud ML, MLOps, model evaluation, and production deployment experience.

Build and operationalize AI/ML threat detection systems for identity, network, device, and session telemetry. Develop real-time streaming pipelines, risk scoring and enforcement systems, MLOps platforms, and security controls for autonomous AI agents. Detect identity compromise, privilege escalation, data exfiltration, prompt injection, agent drift, and unauthorized access. Train, evaluate, deploy, and monitor production ML models while enabling autonomous investigation, containment, and remediation.

Design, develop, and operationalize AI/ML capabilities within secure DoD environments. Build agentic workflows, APIs, production services, and MLOps processes for classification, summarization, enrichment, and intelligent routing of lessons-learned data. Manage model deployment, validation, monitoring, and continuous improvement using Databricks, MLflow, and related tools. Integrate structured and unstructured data, troubleshoot production systems, and collaborate with engineering and mission teams while applying responsible AI and security practices.

Develop, train, evaluate, and optimize machine learning models and data-driven solutions. Prepare data, engineer features, build reusable pipelines, integrate models with APIs and applications, and support deployment, CI/CD, monitoring, testing, and continuous improvement. Collaborate with data engineers, developers, architects, and stakeholders in an Agile environment while documenting technical decisions and mentoring junior developers.

Develop and deploy innovative machine learning and deep learning models for battery, materials, electrochemical, and energy-system applications. Responsibilities include designing architectures, evaluating Transformers and LLMs, conducting experiments and ablation studies, incorporating scientific constraints, tracking research advances, documenting work, and presenting findings. The role collaborates with battery scientists and interdisciplinary R&D teams in a fully on-site environment.

Develop and maintain data integration, ETL, ML, and monitoring pipelines supporting AI/ML and generative AI tools in UCSF Health’s research infrastructure. Productionize models, implement data integrations, design monitoring metrics for safety, bias, drift, performance, and validity, and collaborate with researchers, data scientists, and technical teams. The role requires advanced Python, SQL, MLOps, CI/CD, cloud architecture, and Epic Clarity/Caboodle data-model expertise.

Owns the production ML and GenAI platform, including training and inference pipelines, model serving, registries, monitoring, evaluations, agent gateways, observability, cost controls, and incident response. The role builds reusable deployment patterns, governs agent tools and access, partners with Cloud Engineering, Data Engineering, Security, and SRE, maintains platform architecture, and mentors engineers. Candidates need strong Python and SQL, production ML operations experience, cloud platform expertise, and familiarity with LLM, agent, and MCP architectures.

Build production agentic applications that automate complex geoscience workflows. Responsibilities include designing multi-agent workflows, integrating tools and APIs, managing persistent state, implementing human-in-the-loop controls, enabling retrieval and evidence grounding, fine-tuning LLMs, evaluating and tracing agent performance, and delivering tested web services with CI/CD. The role also requires sound product judgment, collaboration with domain experts, and responsible use of AI coding agents.

Serves as PDW’s senior AI/ML technical leader, defining enterprise AI strategy, architecture, standards, and roadmap while building production systems. Develops RAG solutions, AI agents, orchestration platforms, evaluation frameworks, APIs, data pipelines, and MLOps infrastructure. Leads AI security and governance, optimizes models across cloud and GPU environments, partners across engineering and business teams, and mentors teams adopting AI technologies. Requires extensive hands-on experience with generative AI, production ML systems, secure architecture, and enterprise deployment.

Principal Machine Learning Engineer setting technical strategy for ZoomInfo’s data graph, entity resolution, buying intent, and AI agent context. The role requires hands-on production machine learning, evaluation standards, data contracts, model cost optimization, and scalable Python and SQL systems. Responsibilities include leading design reviews, mentoring engineers, influencing hiring, and establishing technical defaults across teams. Preferred expertise includes ranking, retrieval, knowledge graphs, information extraction, PyTorch model serving, GPU planning, agent memory, and LLM security.

Leads the technical strategy and architecture for Cisco’s agentic AI platforms. Designs hyperscale multi-agent and LLM systems, hybrid and graph-based retrieval, AI safety and observability standards, and automated remediation capabilities. Influences architecture across product organizations, advises executives, mentors senior technical leaders, and represents Cisco at industry forums and standards bodies.

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.