Top AI Engineer Jobs

Lead the design and delivery of production AI systems, agentic workflows, and shared AI platform capabilities for loan origination and servicing. Responsibilities include model selection and routing, MCP and agent interfaces, RAG and context engineering, evaluation and observability, reliability practices, and scalable backend architecture. The role operates independently on complex initiatives, establishes technical patterns, makes platform decisions, and mentors engineers.

Build and maintain premium mobile features, subscription and in-app purchase flows, paywalls, pricing experiences, and advertising functionality for Life360’s consumer app. Own feature quality, performance, reliability, experimentation, and incident response at 100M+ user scale. Collaborate with product, design, data science, and analytics teams; use AI coding agents extensively; contribute to architecture and code reviews; mentor engineers; and help evolve AI-native engineering practices.

Supports and advances institutional AI infrastructure, automation platforms, agents, copilots, bots, cloud services, and integrations. Designs, configures, tests, secures, documents, and maintains AI-enabled systems while evaluating solutions for privacy, accessibility, compliance, and risk. The role also contributes to AI governance, lifecycle management, technical documentation, stakeholder collaboration, training, and strategic planning across the university.

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.

Design and rapidly build Agentforce-powered AI solutions, prototypes, and demonstrations for client engagements. Lead technical client interactions, translate requirements into agent architectures, troubleshoot flows, Apex, integrations, and execution issues, and connect technical outcomes to business metrics. Collaborate with pursuit architects, manage iterative feedback, identify delivery constraints, conduct live demonstrations, and create reusable solution accelerators. The role requires strong enterprise architecture, rapid prototyping, stakeholder communication, and hands-on experience with Salesforce and agentic AI platforms.

Build and demonstrate rapid Agentforce-powered prototypes, agentic architectures, and proof-of-value solutions for clients. Lead technical design, tuning, troubleshooting, live demonstrations, and recurring stakeholder engagements. Translate technical decisions into business outcomes, resolve integration and execution issues under tight timelines, and create reusable accelerators. The role requires client-facing solution engineering, Salesforce and Agentforce expertise, rapid prototyping, enterprise architecture knowledge, and executive communication skills.

Build challenging AI coding tasks from familiar repositories, including task briefs, correct solutions, grading checks, stress tests, and model evaluations. Revise tasks through quality review and produce work suitable for training and assessing AI coding models. Requires at least 10 hours weekly and strong software engineering, testing, repository, and AI coding tool experience.

Design and lead enterprise AI systems using generative AI, RAG, knowledge graphs, and autonomous agents. Define technical strategy, evaluate emerging AI technologies, and guide architecture and roadmaps. Mentor engineers, champion MLOps and LLMOps governance, and ensure security, testing, monitoring, and observability. Collaborate with research, engineering, design, and product teams to deliver scalable customer-facing solutions while optimizing latency, cost, and user experience.

Evaluates AI coding-agent interactions for technical quality, engineering judgment, reasoning, explanations, and user trust. Provides detailed written and recorded feedback, distinguishes response quality levels, and helps define standards for tools such as Codex, Claude Code, and Cursor. This remote contract requires strong TypeScript, JavaScript, or Python experience, familiarity with AI-assisted development workflows, and senior-level engineering judgment.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for engineering judgment, response quality, reasoning, explanations, and usefulness. Provide clear written and recorded feedback, distinguish quality levels, and help define standards for excellent AI-assisted development interactions. This contract role requires senior-level software engineering experience, strong Python or TypeScript/JavaScript skills, familiarity with modern AI development workflows, and excellent English communication.

Evaluate AI coding-agent interactions for correctness, usefulness, reasoning quality, engineering judgment, and user trust. Provide clear written feedback, distinguish response quality levels, help define evaluation standards, and record short video explanations. The role requires senior-level software engineering experience, strong JavaScript/TypeScript or Python skills, familiarity with AI-assisted development tools, and excellent written and spoken English.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for reasoning quality, engineering judgment, explanations, and usefulness. Provide detailed written and recorded feedback, distinguish response quality levels, and help define standards for effective AI-assisted development workflows. This remote contract requires 10–20 hours weekly, with ongoing projects lasting two weeks to several months.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for engineering quality, reasoning, explanations, usefulness, and judgment. Provide clear written and video feedback, distinguish response quality levels, and help define standards for excellent AI-assisted development interactions. This contract role requires senior-level engineering experience, strong communication, familiarity with AI development workflows, and the ability to assess code without executing or reviewing every line.

Design, develop, and maintain scalable full-stack applications across frontend, API, and backend layers. Build AI-native systems using LLMs, retrieval-augmented generation, agentic workflows, and intelligent automation. Translate business requirements into secure, reliable solutions; manage the full software lifecycle; resolve performance and stability issues; and collaborate cross-functionally to deliver enterprise-scale applications.

Designs and leads enterprise AI systems for legal research, including RAG pipelines, knowledge graphs, autonomous agents, and AI platforms. Provides technical strategy, architecture, mentoring, governance, testing, observability, and operational leadership. Partners with research, engineering, design, and product teams to launch scalable, secure AI products. Requires hands-on software engineering expertise in generative AI, cloud-native systems, APIs, distributed systems, and production AI deployments.

Leads development of secure, scalable payment applications using Java, Spring Boot, microservices, AWS, Terraform, and CloudFormation. Designs, codes, tests, debugs, and reviews production software; improves resiliency, automation, and operational stability. Drives responsible adoption of AI-assisted engineering tools, evaluates technical solutions with vendors and internal teams, and mentors software engineers within an agile environment.

Leads the architecture and delivery of large-scale ETL/ELT pipelines, data lakes, distributed Spark workloads, and Java-based services. Designs data quality, governance, lineage, observability, security, and performance-tuning practices. Develops REST APIs and integration components, promotes AI-assisted engineering practices, and drives technical delivery across cross-functional teams. Requires deep Java, Python, big data, Spark, data lake, warehouse, and software engineering expertise, with preferred experience in orchestration, streaming, ACID table formats, production operations, and regulated environments.

Leads the AI engineering team and develops Parametric’s GenAI platform. Responsibilities include managing engineers, translating business needs into AI solutions, designing reusable platform services and APIs, implementing RAG, prompt engineering, fine-tuning, agentic workflows, and building evaluation and monitoring frameworks. The role also oversees AWS deployments, code and design reviews, production support, model optimization, governance, and the complete AI development lifecycle.

Own and architect autonomous CI/CD and QA automation infrastructure for Roku’s iOS and Android mobile applications. Design GitLab CI pipelines, optimize test routing, automate releases, orchestrate physical devices, detect and quarantine flaky tests, and build self-healing systems. Use AI to predict failures, improve pipeline performance, scale agent-authored merge requests, and minimize manual intervention across development and app-store submission.

The Senior Engineer II, Machine Learning will develop and implement machine learning models, analyze multiomic/single-cell data, and apply image processing techniques for research projects.

Own the architecture and engineering of production AI/ML infrastructure powering Relay. Build model-serving, inference, evaluation, orchestration, deployment, observability, and lifecycle systems. Optimize model performance, latency, throughput, scalability, reliability, and cost while establishing feedback loops for continuous quality improvement. Partner with engineering and product teams to advance agentic AI capabilities and make foundational architectural decisions in a rapidly evolving environment.

Build and deploy production machine learning models, scalable ML infrastructure, real-time training and inference pipelines, multimodal AI systems, and model APIs. Optimize model performance for cost, speed, and accuracy while applying MLOps practices. Collaborate with data science teams to productionize research models and independently architect highly available ML systems for a global media and entertainment platform.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security mission requirements. Integrates data pipelines, evaluates model performance, applies statistical analysis, ensures data and output quality, and transforms prototypes into scalable production solutions. Provides system integration oversight and may lead distributed teams. Requires advanced education or equivalent experience and an active TS/SCI clearance with polygraph.

Designs and evaluates data architectures, ETL pipelines, storage strategies, and governance practices supporting Marine Corps AI capabilities. Advises leadership and vendors on data maturity, security, rights, interoperability, and deployment across cloud, on-premises, and tactical edge environments. Establishes data quality standards, analyzes data flows, supports experiments and wargames, and documents technical assessments. Requires substantial data engineering experience, U.S. citizenship, and TS/SCI eligibility.

Build and deploy AI-powered workflows, agents, copilots, and automations across revenue, marketing, and customer success. Integrate CRM, BI, communication, and data systems; enable natural-language analytics; establish guardrails, monitoring, documentation, and feedback loops; and measure adoption, productivity, revenue impact, and ROI. The role requires hands-on LLM, API, coding, automation, and GTM systems experience, plus cross-functional project leadership and end-to-end ownership.