Top AI Engineer Jobs

Design, build, and operate secure enterprise AI platform capabilities connecting models, agents, and internal systems. Own major infrastructure workstreams (MCP gateway, enterprise context), build backend services and APIs, enable identity-aware governance (access, permissions, auditing), design agentic workflows, and take operational ownership while partnering with stakeholders to scale dependable AI tooling across Spotify.

Embed with strategic customers to design, build, and deploy frontier AI/LLM systems into production. Responsibilities include discovery, architecture, integration, prototyping, deployment, observability, safety evaluations, and driving adoption and product feedback.

Build and maintain marketing data pipelines, replace spreadsheet workflows with structured databases, automate data cleaning/deduplication and syncing, develop AI-driven reports and dashboards, and collaborate with marketing, AdOps, and engineering to ensure reliable, unified, AI-ready marketing data.

Design, develop, and maintain AI/ML software solutions; collaborate with developers and leadership; build tools for analyzing large-scale data; debug, profile, and optimize code; create documentation and user support materials; leverage modern LLM tools to accelerate development.

Develop, debug, and maintain AI applications and tools alongside engineers. Design solutions, build data analysis tools, optimize performance, create documentation, and support users.

Design, develop, and deploy ML solutions for localization workflows (machine translation, LLM fine-tuning, QA). Own projects end-to-end, implement and optimize models using Python and ML frameworks, deploy via Docker and AWS (SageMaker/EC2/S3), monitor production, document experiments, and collaborate across teams.

Lead design, implementation, optimization, and administration of scalable database systems and AI/ML data infrastructure. Build vector DBs, embedding stores, RAG architectures, ETL pipelines, and integrations with LLM APIs. Provide technical leadership, performance tuning, security, data governance, and support for AI-enabled applications while mentoring junior staff and driving data quality and monitoring.

Design, build, and operate scalable data pipelines and storage/indexing for an AI-driven legal platform. Implement queue-based asynchronous processing, multi-tenant architectures, observability, migration strategies, and system design documentation. Collaborate across engineering, product, and security to ensure data reliability, performance, and operational readiness.

Work with customers to discover high-value AI use cases, design and prototype LLM- and agent-based solutions, build demos and pilots, and support transition to production. Provide technical architecture, documentation, training, governance guidance, and hands-on implementation support across integrations, automation, and evaluation to enable secure, explainable, and measurable AI adoption.

Design, build, and operate optical metrology and test systems for flight hardware; automate test procedures and data collection; troubleshoot optics and laser systems; develop test plans, fixtures, production procedures, and transfer assemblies to high-rate production; participate in validation, lot acceptance, qualification, and reliability testing.

Define low-level technical requirements and validate AI-enabled features for an enterprise application. Work with customers, systems engineers, developers, and stakeholders to convert requirements into Jira user stories/epics, perform functional testing, and advise on AI approaches and integrations. Engage in Agile delivery and cloud-based deployments (AWS).

Senior ML Engineer joins a product team to design, build, deploy, and monitor production-grade AI/ML solutions. Responsibilities include algorithm and software design, pairing with teammates, creating scalable data pipelines and infrastructure, implementing model serving and monitoring, performing performance tuning and testing, and supporting product lifecycle and stakeholder collaboration.

Design, develop, and deploy production-grade AI/ML systems (NLP/LLMs, CV, generative models). Build scalable data pipelines, microservices/APIs, and cloud/edge deployments; optimize, version, and evaluate models; integrate solutions into classified operational environments and produce technical documentation.

Design, create, and implement AI and ML systems for Defense and Intelligence sectors, ensuring scalability and integration into various environments.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Hybrid technical/customer-facing role building and refining AI risk-assessment modules using LLMs, writing SQL, and creating Power BI dashboards. Lead client onboarding, translate safety programs into the platform, partner on scoping and SOWs, serve as primary technical contact, QA AI outputs, and drive product improvements from client feedback.

Lead full-stack development and architecture of a SaaS platform, design and implement AI agent integrations, mentor engineers, own system and DB design (including PostGIS), ensure CI/CD, observability, security, and scalable cloud deployments.

Build end-to-end agent-powered tools and full-stack features using Typescript/Python. Integrate LLMs, vector databases, and agentic frameworks, develop reliable backend services, work with distributed systems and cloud architectures, and ship production solutions in tight experimentation loops with cross-functional partners.

Lead design, development, and production deployment of complex AI systems (LLM apps, retrieval pipelines, model-driven services). Own technical direction, ensure security/privacy/responsible AI, drive MLOps and reliability, mentor engineers, and partner with product and business stakeholders to deliver scalable, governed AI solutions.

Lead design and delivery of polished frontend experiences for the CoCo coding agent platform. Define architecture and design abstractions, build scalable high-performance UI systems, collaborate cross-functionally, drive product vision and planning, and coordinate features with infrastructure and regional teams.

Lead fast-paced full‑stack development using AI coding tools (Claude, Copilot) to prompt, review, harden, and ship production-ready features. Translate requirements into prompts/specs, orchestrate multi-agent AI workflows, validate AI-generated code for correctness, security, and performance, and collaborate closely with product and design in compressed sprints.

Build and test AI-enabled prototypes, internal tools, prompts, and agentic automations. Translate requirements into small software components, write basic unit tests, support prompt engineering and API integrations, document designs and runbooks, participate in code reviews and demos, and follow data privacy, security, and responsible AI practices while learning SDLC and team processes.

Lead development and deployment of AI/ML models and algorithms (LLMs, CNNs, transformers) across the ML pipeline. Prototype, test, document, and productionize solutions for autonomy, perception, decision support, and sensor/data fusion. Collaborate with cross-functional teams and present technical results; work with web frameworks and software engineering best practices.

The Applied AI Software Engineer will evaluate LLM-based agents, manage benchmarking, and ensure performance, safety, and reliability in healthcare settings. Responsibilities include designing evaluation plans, running experiments, collaborating with teams, and developing internal tooling for AI features.

Design, implement, and release workflows for AI security products. Collaborate across platforms, utilizing cloud and AI technologies. Act as a technical expert, driving process improvement and supporting application development.