Ai Agent Engineering

Ai Agent Engineering Jobs

7 jobs found

Today

Staff Machine Learning Engineer

ServiceNow Santa Clara, CA

Santa Clara, CA

Today

$176k - $308k/yr Full time Backend Developer Agent Orchestration Agent Runtime

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Job Description

ServiceNow is hiring a Staff Machine Learning Engineer for the Agent Orchestration organization in Santa Clara, CA (onsite).

Responsibilities

Own major portions of the agent execution harness, the infrastructure layer that enables enterprise agents to reason over real data and run safely at Fortune 500 scale
Design and build the orchestration layer that routes inputs, manages context, invokes tools, handles retries, and exposes execution state across multi-step workflows
Drive runtime reliability with designs for fault tolerance, latency, and throughput for enterprise workflows that cannot fail silently
Instrument the harness with tracing, cost attribution, and latency visibility to support production debugging and proactive failure detection
Develop prompt management capabilities including versioning, templating, and systematic evaluation to maintain stable behavior across model updates and configuration changes
Create and own evaluation frameworks spanning unit evals, integration evals, and production monitors to measure quality, detect regressions, and inform decisions
Integrate and abstract over frontier LLMs, managing model routing, fallback strategies, and production tradeoffs for cost and latency
Set technical standards via architecture decisions, code reviews, and technical coaching, with emphasis on agentic design patterns and production AI discipline
Define and enforce where agent logic resides, including tool calls, sub-agent boundaries, hardcoded paths, and human escalation patterns

Requirements

7+ years building production software systems with proven reliability, performance, and scalability outcomes
Hands-on experience shipping generative AI products in production, with responsibility for AI-powered features used by production customers
Strong understanding of large language model behavior, including failure modes and context constraints, and how prompt design shapes behavior at scale
Experience with Python and Kubeflow
Experience with SageMaker, Vertex AI, or AzureML, plus SQL
Practical prompt engineering experience across versioning and evaluation, including prompt updates and A/B evaluation cycles
Demonstrated track record in eval engineering, with evaluation suites shipped and used to drive production quality decisions
Cost and efficiency awareness, including reasoning about model routing, inference cost, and latency tradeoffs in production
Strong software engineering fundamentals including distributed systems, API design, and testing practices
Comfort operating in fast-moving, ambiguous AI product environments

Technologies

Python
Kubeflow
SageMaker
Vertex AI
AzureML
SQL
LangChain
LlamaIndex

Benefits

Health plans, including flexible spending accounts
401(k) Plan with company match
ESPP
Matching donations
Flexible time away plan
Family leave programs
Base pay: $176,100 - $308,200 per year, plus equity (when applicable), variable/incentive compensation, and benefits

Nice to Have

Experience with multi-agent coordination patterns (A2A, MCP)
Familiarity with agent frameworks (LangChain, LlamaIndex, or similar)
Prior experience shipping AI systems in enterprise software
Experience with AI observability tooling (tracing, cost tracking, LLM-specific monitoring)
Familiarity with cloud-native infrastructure and reliability practices, including service observability, logging, monitoring, and production troubleshooting

Work Location / Work Personas

Work personas are assigned based on the nature of the work and assigned work location (flexible, remote, or required in office)
ServiceNow may confirm distance between primary residence and the closest ServiceNow office using a third-party service to determine eligibility for a work persona

Equal Opportunity Employer

ServiceNow is an equal opportunity employer
All qualified applicants receive consideration without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or other protected categories under law
Applicants with arrest or conviction records will be considered in accordance with legal requirements

Accommodations

If you need a reasonable accommodation to complete any part of the application process, or cannot use the online application and need an alternative method, contact globaltalentss@servicenow.com

Export Control Regulations

For roles requiring access to controlled technology, ServiceNow may need to obtain export control approval from government authorities for certain individuals
Employment is contingent upon ServiceNow obtaining any required export license or other approval under relevant export control authorities

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2d ago

Google Cloud AI Engineer

Experis Hartford, CT

Hartford, CT

2d ago

$70 - $75/hr Full time Agentic Ai Ai Agent Ai Agent Engineering

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Job Description

Experis is hiring a Senior AI Solutions Engineer to lead forward-deployed engagements delivering production-ready agentic AI in the insurance industry using Google Cloud. The role focuses on designing, building, scaling, and evaluating LLM applications with Vertex AI, Gemini, BigQuery, Agent Builder, Cloud Run, and related services.

Responsibilities

Lead forward-deployed engagements from discovery through production deployment for agentic AI solutions in the insurance industry.
Design, build, and scale GenAI and Agentic AI solutions using Google Cloud technologies including Vertex AI, Gemini, BigQuery, Agent Builder, Cloud Run, and related services.
Convert underwriting, claims, pricing, distribution, and servicing challenges into production-ready AI products that deliver measurable business outcomes.
Apply strong LLM application design practices covering prompt engineering, agent orchestration, RAG architectures, evaluation frameworks, and AI governance.
Prototype quickly and iterate to move from concept to working demonstrations within days, contributing reusable accelerators and best practices across engagements.

Requirements

5–10 years of experience in solution engineering, AI engineering, forward-deployed engineering, consulting, or enterprise application development, preferably in client-facing roles.
Hands-on experience building GenAI and Agentic AI solutions on Google Cloud, including Vertex AI, Gemini, BigQuery, Agent Builder, Cloud Run, and related services.
Strong understanding of LLM application design, prompt engineering, agent orchestration, RAG architectures, evaluation frameworks, and AI governance.
Experience designing and integrating enterprise applications, APIs, data platforms, and workflow solutions.
Insurance domain expertise in Property & Casualty, Life, Reinsurance, or Specialty Insurance operations, including familiarity with underwriting, claims, pricing, actuarial, policy administration, customer servicing, or distribution/broker operations.

Technologies

Google Cloud
Vertex AI
Gemini
BigQuery
Agent Builder
Cloud Run
Prompt engineering
Agent orchestration
RAG architectures
Evaluation frameworks
AI governance
APIs

Location and Pay

Location: Hartford, CT (onsite)
Pay range: USD 70–75 per hourly

Benefits

Deliver agentic AI solutions aimed at measurable impact across underwriting, claims, and customer-facing workflows.
Work in a forward-deployed environment with close collaboration between business and technology stakeholders to drive outcomes.
Build reusable accelerators and best practices to strengthen delivery across multiple engagements.
Use modern Google Cloud AI capabilities to design, prototype, and deploy production-ready solutions.
Join a team that values experimentation, rapid iteration, and clear communication with executives and technical teams.

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3d ago

AI Engineer

Oakwood Systems Group Inc. Kansas City, MO

Kansas City, MO

3d ago

Full time Backend Developer Agentic Ai Ai Agent

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Job Description

Oakwood Systems Group Inc. is seeking a hands-on, experienced AI Engineer to design, build, and deploy secure, production-ready AI applications, copilots, and agents on the Microsoft cloud. The role emphasizes generative AI delivery, retrieval-augmented generation (RAG), agentic workflows, enterprise integration, and measurable production evaluation.

Responsibilities

Design, build, and deploy production AI applications, copilots, and agents using Microsoft Azure and modern AI frameworks.
Build RAG and enterprise knowledge solutions using embeddings, vector search, document processing, metadata, and retrieval strategies that support grounded, source-aware answers.
Design agentic workflows that use tools, APIs, structured outputs, memory or state, human approval, and multi-step orchestration to automate business processes safely.
Integrate AI capabilities with enterprise systems and data sources through REST APIs, Microsoft Graph, Power Platform, databases, SaaS platforms, and custom services.
Develop maintainable backend services and APIs, primarily in Python, with testing, error handling, logging, security, and performance considerations.
Implement evaluation approaches for AI quality, groundedness, reliability, safety, latency, and cost, using results to improve prompts, retrieval, orchestration, and model selection.
Apply production engineering practices including source control, automated testing, CI/CD, containerization, secrets management, observability, and environment promotion.
Apply enterprise security and governance practices, including identity, role-based access, data boundaries, auditability, responsible AI controls, and human-in-the-loop patterns where appropriate.
Collaborate with client business and technical stakeholders to understand workflows, clarify requirements, prototype solutions, explain tradeoffs, and translate AI goals into deployable designs.
Contribute to architecture reviews, code reviews, estimation, technical discovery, and solution design for new client opportunities.
Create reusable patterns, accelerators, reference implementations, and delivery assets to improve consistency across future Oakwood AI engagements.
Document solutions clearly and support knowledge transfer so client teams can operate and extend delivered systems.
Mentor less-experienced engineers and help establish pragmatic engineering standards for production AI delivery.

Requirements

5+ years of professional engineering experience in software engineering, machine learning engineering, data engineering, applied AI, backend development, or a related discipline, with meaningful ownership of production systems.
2+ years building generative AI / LLM-powered applications or AI-enabled software, including at least one solution taken beyond prototype into a production or production-like environment.
Strong Python skills and experience building production services, APIs, integrations, or data and AI pipelines.
Hands-on experience with LLM application patterns including prompt design, RAG, embeddings, vector search, structured outputs, function or tool calling, and agent orchestration.
Experience evaluating AI systems using measurable criteria (quality, reliability, safety, latency and/or cost) rather than subjective demos alone.
Experience with cloud-based applications and AI services, with strong Azure experience preferred and ability to work effectively in a Microsoft-centric environment.
Working knowledge of SQL and enterprise data integration, including reasoning about data quality, access, lineage, and data architecture impacts on AI outcomes.
Experience with modern engineering practices including Git, peer review, automated testing, CI/CD, containers, APIs, logging and monitoring, and secure configuration and secrets management.
Ability to troubleshoot production AI behavior such as retrieval failures, hallucinations, tool-calling errors, prompt regressions, latency, cost, and unexpected edge cases.
Strong client-facing communication skills to explain technical concepts, architecture decisions, risks, and tradeoffs to engineers and business stakeholders.
Ability to work independently in a consulting environment, manage competing priorities, and move from incomplete requirements to a structured implementation plan.
Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent practical experience.

Technologies

Microsoft Azure
Python
REST APIs
Microsoft Graph
Power Platform
SQL
Git, CI/CD, containers
Microsoft Foundry, Azure AI Foundry, Azure OpenAI, Azure AI Search
Copilot Studio, Power Automate
Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI
Model Context Protocol (MCP)
Azure Functions, Container Apps, App Service, AKS, API Management
Cosmos DB, Azure SQL, Key Vault, Entra ID
Microsoft Fabric, Databricks
GitHub Copilot, Claude Code, Codex, Cursor

Preferred Qualifications

Hands-on experience with Microsoft Foundry / Azure AI Foundry, Azure OpenAI, Azure AI Search, Copilot Studio, Power Automate, or related Microsoft AI services.
Experience with one or more orchestration frameworks such as Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI, or custom agent frameworks.
Experience with Model Context Protocol (MCP), agent-to-tool integration patterns, or reusable enterprise tool registries.
Experience with Azure application services including Functions, Container Apps, App Service, AKS, API Management, Cosmos DB, Azure SQL, Key Vault, and Entra ID.
Experience with MLOps / LLMOps practices including evaluation pipelines, model or prompt versioning, tracing, monitoring, feedback loops, and production incident response.
Experience with Microsoft Fabric, Databricks, or other modern data platforms used to prepare governed enterprise data for AI.
Experience with classical machine learning, NLP, deep learning, fine-tuning, multimodal models, or model serving where the use case requires more than foundation-model APIs.
Experience using AI-assisted development tools such as GitHub Copilot, Claude Code, Codex, or Cursor while applying disciplined engineering review for correctness, security, and maintainability.
Prior consulting, professional services, systems integration, or Microsoft partner experience.
Relevant Microsoft or cloud certifications such as Azure AI Engineer, Azure Developer, Azure Solutions Architect, or equivalent.

What Success Look Like

AI solutions move from discovery and prototype to secure, supportable production deployments with clear acceptance criteria.
Clients can articulate the business value of delivered systems, not only technical capability.
AI quality improves through repeatable evaluation, observability, and feedback rather than one-time demonstrations.
Solutions integrate cleanly with client applications, data, identity, and operating processes and remain maintainable after handoff.
Reusable engineering patterns and delivery assets reduce effort and risk across future engagements.
Clients and teammates view the engineer as a trusted delivery partner combining technical depth, sound judgment, and practical execution.

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4d ago

Staff Applied AI Engineer

Qualified Health PBC Palo Alto, CA

Palo Alto, CA

4d ago

$200k - $260k/yr Full time Agentic Ai Ai Agent Ai Agent Engineering

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Job Description

Qualified Health PBC is hiring a Staff Applied AI Engineer in Palo Alto, CA (onsite) to lead technical ownership for agentic workflow infrastructure across product lines.

Responsibilities

Architect and build core agentic workflow infrastructure supporting 100+ workflows across multiple product lines.
Define team-wide technical standards and reusable patterns for agentic workflow development, including eval pipelines, prompt frameworks, and deployment patterns.
Lead design and code review for complex, cross-cutting integrations and production services.
Own evaluation and adoption decisions for state-of-the-art LLMs (OpenAI, Anthropic, Mistral, etc.) across use cases, including build-vs-buy tradeoffs.
Mentor engineers on production-grade agentic system design, MLOps, and shipping efficiently while maintaining reliability.
Partner with product, engineering, and design leadership to shape the technical roadmap for AI-powered features.
Identify and resolve systemic bottlenecks in workflow infrastructure before they limit broader team execution.

Requirements

Strong software engineering foundation; Python required and deep familiarity with production system design.
5+ years building production software, including 2+ years building AI-powered applications with LLMs (ChatGPT, Claude, Anthropic, Mistral, etc.).
Proven experience architecting REST APIs and services deployed at scale in cloud environments (GCP preferred).
Strong grasp of MLOps, model deployment, and workflow automation, including experience setting standards or infrastructure other engineers build on.
Track record of driving technical decisions with significant ambiguity and minimal oversight.

Technologies

Python
OpenAI
Anthropic
Mistral
ChatGPT
Claude
REST APIs
GCP
LLMs

Desirable skills

Bachelor’s + 7 years OR Master’s + 5 years in Computer Science, Engineering, or related field (or equivalent experience).
Background spanning both software engineering and ML/AI systems.
Experience designing eval pipelines, prompt tuning frameworks, or automation infrastructure at a platform level.
Startup experience with a bias toward shipping fast and iterating.
Exposure to both startup agility and the engineering rigor of a mature, at-scale codebase.
Experience mentoring engineers or setting technical direction for a team.

Pay & benefits

Salary range: $200,000 - $260,000 per year, depending on skills, qualifications, experience, and location.
Eligible for equity and benefits.
Competitive salaries with equity packages.
Robust medical/dental/vision insurance.
Flexible working hours.
Hybrid work options.
Inclusive environment that fosters creativity and innovation.

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4d ago

Senior Applied AI Engineer

Microsoft Redmond, WA

Redmond, WA

4d ago

$120k - $261k/yr Full time Senior Agentic Ai Ai Agent

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Job Description

The Senior Applied AI Engineer role at Microsoft (ACES CØDE) focuses on designing, building, and operating production agentic AI services that improve AI-first support experiences at Azure scale.

Responsibilities

Analyze data signals to identify patterns across support-case trends, customer issue patterns, and authorized technical signals at scale, uncovering recurring failure patterns and gaps in Azure diagnostics and documentation.
Develop scalable agentic AI solutions for Azure diagnostics and documentation using LLM orchestration, RAG pipelines, agent workflows, tool integrations, and memory and state management, including human-in-the-loop experiences.
Write, review, test, and maintain production-quality C# and Python code.
Create reusable components, reference implementations, and engineering patterns that accelerate delivery across teams.
Translate customer support scenarios and product requirements into technical designs, then deliver end-to-end implementation, testing, deployment, monitoring, and continuous improvement.
Develop evaluation frameworks using rubrics, golden datasets, simulations, judge models, and offline and online evaluation.
Use measurable quality thresholds to transition agent capabilities from experimentation into production operations.
Ensure services meet requirements for correctness, reliability, availability, latency, scalability, maintainability, cost, and safety.
Apply privacy protections and agent-security controls, including prompt-injection defenses, least-privilege tool access, identity and authorization controls, data boundary enforcement, approval gates, auditability, and fail-safe behavior.
Ground agent responses and actions in trusted, current signals by integrating with Azure data sources, APIs, and cloud-native services.
Implement CI/CD and automated testing with feature flags, staged rollouts, tracing, logging, metrics, and alerting.
Diagnose production issues, participate in incident response, and drive corrective improvements.
Evaluate models, frameworks, prompts, retrieval approaches, and architectural options using experiments and production telemetry.
Balance quality with reliability, latency, cost, maintainability, and operational complexity.
Instrument solutions and use results to improve automation quality, case-volume reduction, resolution time, engineering efficiency, and customer satisfaction.
Lead design reviews, resolve technical challenges, coordinate implementation across contributors, and align partner teams around shared interfaces and delivery goals.
Support other engineers through code reviews, technical guidance, reusable documentation, and practical coaching in applied AI and production software engineering.
Partner with product managers, researchers, platform engineers, Azure service teams, and support-domain experts to deliver durable solutions and influence future platform capabilities.

Requirements

Bachelor's Degree in Computer Science or a related technical field AND 4+ years of technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Ability to meet Microsoft, customer and/or government security screening requirements for this role.
Microsoft Cloud Background Check: required to pass upon hire/transfer and every two years thereafter.

Preferred Qualifications

Experience building or integrating with LLM-based systems, including prompt engineering, RAG, or agent frameworks.
Familiarity with Azure services such as Azure OpenAI, Azure AI Foundry, Azure Functions, Cosmos DB, or comparable cloud-native technologies.
Experience with CI/CD pipelines, automated testing, and production observability.
Experience with Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, or comparable enterprise AI platforms.
Experience designing AI evaluation systems using rubrics, golden datasets, judge models, simulation, adversarial testing, and offline or online evaluation.
Experience with RAG systems, including indexing, retrieval, ranking, grounding, citation, knowledge freshness, and access control.
Experience implementing agent-security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.
Experience building reliable distributed services with standards for scalability, resiliency, performance, observability, and cost efficiency.
Experience with infrastructure as code, container orchestration, feature management, and staged production deployment.
Demonstrated ability to lead technical execution, influence design decisions, and mentor engineers without formal people-management authority.
Experience collaborating across product, platform, research, and partner engineering teams on complex co-development initiatives.
Strong business judgment with experience linking engineering investments to customer and operational outcomes.
Background in customer support engineering, supportability, diagnostics, or customer-experience platforms is a plus.

Technologies

C, C++, C#, Java, JavaScript, Python
LLM orchestration, RAG pipelines, agent workflows
Azure OpenAI, Azure AI Foundry, Azure Functions, Cosmos DB
CI/CD, Microsoft Agent Framework, feature management
Container orchestration, infrastructure as code
Prompt engineering, rubrics, golden datasets, judge models, simulations
Offline evaluation, online evaluation
Azure data sources
Tracing, logging, metrics, alerting

Location and Compensation

Location: Redmond, WA (onsite)
Salary: USD 119,800 - 261,000 per year

Minimum Education

Bachelor's Degree in Computer Science or related technical field.

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6d ago

AI Engineer 5 (Gen AI Platform Services: Agentic AI, Guardrails, Evaluation)

Capital One New York, NY

New York, NY

6d ago

$251k - $286k/yr Full time Backend Developer Agentic Ai Ai Agent

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Job Description

The AI Engineer 5 role at Capital One focuses on building responsible and reliable GenAI platform services. The position contributes to AI software components that support foundation model training, LLM inference, agentic workflows, guardrails, and model evaluation using scalable cloud and open-source technologies.

Responsibilities

Collaborate with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that improve how associates work and how customers interact with Capital One.
Design, develop, test, deploy, and support AI software components, including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, and model evaluation, along with related capabilities such as experimentation, governance, and observability.
Use an engineering stack that includes Open Source and SaaS AI technologies, including AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
Develop and introduce state-of-the-art foundation model optimization techniques to improve scalability, cost, latency, and throughput for large scale production AI systems.
Contribute to technical vision and long-term roadmap planning for foundational AI systems at Capital One.
Design and optimize multi-model orchestration pipelines by integrating LLMs, vector search, and domain-specific models into unified systems.
Lead cost-performance governance reviews across AI systems, including tracking GPU utilization, model throughput, and inference cost efficiency.
Participate in and lead team design councils or design review boards to support technical consistency and compliance with AI engineering standards.
Mentor Principal and Manager-level AI engineers to support cross-domain learning and increase organizational technical maturity.

Requirements

Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, plus at least 6 years of experience developing AI and ML algorithms or technologies; or a Master’s degree in the same or related fields, plus at least 4 years of experience developing AI and ML algorithms or technologies.
At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.

Technologies

AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, AWS, Google Cloud, Azure
Python, Go, Scala, CUDA, Java, C++, C#, Golang
Rule-based, retrieval-augmented, generative components

Preferred Qualifications

Experience leading development AI systems with tradeoff decisions across cost, latency, throughput, and accuracy.
7+ years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
Experience designing, developing, delivering, and supporting complex AI systems.
Experience building AI and ML algorithms or technologies (for example, LLM inference, similarity search and VectorDBs, guardrails, memory) using Python, C++, C#, Java, CUDA, or Golang.
Experience applying state-of-the-art techniques to optimize training and inference software for improved hardware utilization, latency, throughput, and cost.
Experience building agentic AI systems and agentic workflows.
Knowledge of recent AI research and the ability to apply novel techniques in production.
Excellent communication and presentation skills, including the ability to explain complex AI concepts to peers.
Experience architecting and integrating heterogeneous AI systems that combine rule-based, retrieval-augmented, and generative components into unified production pipelines.
Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes.
Ability to balance model performance with operational cost using dynamic inference strategies and model compression.
Experience right-sizing models, instance counts, and hardware types based on requirements such as context length and token inputs/outputs.

Benefits

Health, financial and other benefits that support your total well-being.
Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
Comprehensive, competitive, and inclusive set of health, financial and other benefits.

Role Details

Location: New York, NY (onsite)
Salary: USD 250,800 - 286,200 per yearly
Minimum Experience: 6 years

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7d ago

Senior Applied AI Engineer, Agent Tools & Capabilities

Apple Cupertino, CA

Cupertino, CA

7d ago

$150k - $278k/yr Full time Senior Ai Agent Engineering Artificial Intelligence

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Job Description

As a Senior Applied AI Engineer for Siri and Apple Intelligence, you will shape how agentic models reason, plan, and carry out actions across Apple’s ecosystem. You will own the end-to-end quality story and build the platform, runtime, and API abstractions that enable reliable agent capabilities under strict constraints.

Role Responsibilities

Develop and optimize the platform and runtime that power Siri while meeting strict latency, memory, and power constraints
Improve agent capabilities using systematic, data-driven experimentation
Lead a quality loop from early-stage feature development through hillclimbing based on field data
Collaborate with Apple app and frameworks teams to integrate their experiences into Siri and translate their requirements into platform direction
Work effectively in ambiguous, rapidly changing conditions in a frontier AI environment

Required Qualifications

Experience in prompt engineering, tool representation, or tool retrieval
Experience designing evals and improving model quality with data
Familiarity with App Intents or other Apple frameworks
Strong API design skills and a track record of building platforms for other developers
MS or PhD in Computer Science, Machine Learning, or a related field
Bachelor's degree in Computer Science, Machine Learning, or a related field, plus 3+ years of industry experience
Strong programming skills in Swift, C++, Objective-C, or Python
Hands-on experience shipping production systems built around LLMs
Knowledge of agentic systems, including planning, orchestration, and multi-step tool use
Ability to communicate and deliver in fast-moving, ambiguous environments

Technologies

Swift
C++
Objective-C
Python
LLMs
App Intents

Preferred Qualifications

Experience in prompt engineering, tool representation, or tool retrieval
Experience designing evals and improving model quality with data
Familiarity with App Intents or other Apple frameworks
Strong API design skills and a track record of building platforms for other developers
MS or PhD in Computer Science, Machine Learning, or a related field

Pay & Benefits

Base pay range: USD 150,400 - 277,600 per year
Discretionary employee stock programs, with opportunity to become an Apple shareholder
Eligible discretionary restricted stock unit awards
Employee Stock Purchase Plan: purchase Apple stock at a discount if voluntarily participating
Comprehensive medical and dental coverage
Retirement benefits
Range of discounted products and free services
Reimbursement for certain educational expenses, including tuition, for formal education related to advancing your career at Apple
Discretionary bonuses or commission payments, and relocation may be eligible
Learn more about Apple Benefits (as stated)

Additional Notes

Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. Base pay is determined within the posted range based on skills, qualifications, experience, and location.

Location

Cupertino, CA (onsite)

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Ai Agent Engineering Jobs

Staff Machine Learning Engineer

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Senior Applied AI Engineer

AI Engineer 5 (Gen AI Platform Services: Agentic AI, Guardrails, Evaluation)

Senior Applied AI Engineer, Agent Tools & Capabilities

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