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Machine Learning Engineer, Sales Engineering

Apple

Austin, TXFull-timeSeen 1w agoSeen in employer's feed 5 days ago

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At a glance

Compensation
No compensation found
Location
Austin, TX
Schedule
Full-time
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Job overview

Apple’s Sales Engineering Rapid Application Development team seeks a Machine Learning Engineer to build and deploy scalable ML solutions for its global Channel Sales organization. The role focuses on generative AI, large language and multimodal models, and production-ready applications, including RAG-based systems. The engineer will collaborate with product, design, and engineering teams to train, deploy, and optimize ML-powered applications.

Skills & qualifications

RequiredNice to have

Skills

LLM Development, requiredMachine Learning Deployment, requiredLLM Fine-Tuning, nice to havePython, nice to havePyTorch, nice to haveTensorFlow, nice to haveHugging Face Transformers, nice to haveRetrieval-Augmented Generation, nice to haveLangChain, nice to haveLlamaIndex, nice to haveDistributed Computing, nice to haveCloud Platforms, nice to haveDocker, nice to haveKubernetes, nice to haveProblem-Solving, nice to haveTechnical Communication, nice to haveAgent-Based Systems, nice to haveLLM Serving, nice to haveInference Optimization, nice to haveDistributed Systems, nice to haveCloud-Native Infrastructure, nice to haveMachine Learning, nice to haveSales Domain Knowledge, nice to have

Qualifications

MS or BS in Related Field or Equivalent Experience, required5+ Years ML Solution Experience, required5+ Years LLM Experience, required

Full job description

Weekly Hours: 40

Role Number: 200691752-0157

Summary

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. Apple’s Sales Engineering team is shaping the future of Channel Sales with innovative, high-impact applications. We’re looking for a Machine Learning Engineer to help us design and build the next generation of intelligent systems that power Apple’s global partner ecosystem. In this role, you’ll develop and deploy machine learning solutions while leveraging generative AI and advanced ML capabilities to deliver scalable, production-ready systems that accelerate strategic, high-impact initiatives across Apple Channel Sales. If you’re passionate about applying AI to solve complex business problems, experimenting with emerging GenAI technologies, and building products that make a real difference, join our collaborative team and help us move fast on game-changing ideas.

Sales and Operations Engineering is part of IS&T and drives the technology behind Apple's global operations, sales, and supply chain. The team connects the systems that move products from factory to customer — bridging sales platforms with the operational infrastructure that keeps Apple running at scale and ensuring Apple's technology and business priorities move in lockstep. We are looking for people with consistent record of running complex cross functional projects under demanding timelines. This is a hands-on position where the Project Manager will be involved in building strong relationships with stakeholders, requirement analysis, functional and technical design discussions, driving project deadlines, and communicating project status to senior leadership.

Description

Apple’s Sales Engineering Rapid Application Development (RAD) team is looking for a Machine Learning Engineer to build intelligent, scalable solutions that power Apple’s global Channel Sales. You’ll leverage generative AI and advanced machine learning technologies to deliver high-performance, production-ready systems that drive measurable business impact. The ideal candidate blends deep ML expertise with strong engineering skills, is passionate about applying AI to solve real-world problems, and thrives in fast-paced environments delivering value quickly. You’ll work side by side with product, design, and engineering teams to design, train, deploy, and optimize ML-powered applications that push the boundaries of innovation—whether enabling GenAI-driven workflows, implementing RAG-based systems, or pioneering new intelligent capabilities. If you’re excited about shaping impactful AI solutions in a collaborative, experiment-driven environment, Sales Engineering RAD team is where you’ll thrive.

Minimum Qualifications

  • MS / BS in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical field, or equivalent experience.

  • 5+ years experience developing and deploying machine learning solutions, with a strong focus on Large Language Models (LLMs) or Large Multimodal Models (LMMs).

  • 5+ years experience with LLMs and transformer-based architectures (e.g., BERT, GPT, LLaMA).

Preferred Qualifications

  • Proven ability to fine-tune, adapt, and deploy LLMs/LMMs into real-world, production-grade applications.

  • Proficiency in Python and leading ML frameworks such as PyTorch and TensorFlow.

  • Hands-on experience leveraging Hugging Face Transformers and associated libraries.

  • Solid understanding of Retrieval-Augmented Generation (RAG) and practical experience with orchestration frameworks like LangChain or LlamaIndex.

  • Familiarity with distributed computing, cloud platforms (AWS, GCP, Azure), and containerization/orchestration tools (Docker, Kubernetes).

  • Exceptional problem-solving skills and the ability to articulate complex ML/AI concepts clearly and effectively to diverse audiences.

  • Experience extending beyond traditional LLMs/LMMs to include agent-based systems and agentic workflows.

  • Proficiency with advanced LLM serving and inference frameworks, ensuring scalable and efficient model deployment.

  • Practical experience building sophisticated RAG applications and orchestrating complex LLM pipelines from inception to deployment.

  • Working knowledge of distributed systems and cloud-native infrastructure.

  • Expertise in optimizing transformer-based architectures (e.g., BERT, GPT, LLaMA) for low latency, high-performance inference.

  • Demonstrated ability to communicate complex technical results and ML/LLM concepts with clarity and impact to both technical and non-technical stakeholders.

  • Experience applying ML methodologies in specific domains, such as sales.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.

How this job compares

Set against the 6,196 open Machine Learning Engineer jobs on Olive Jobs, counted November 11, 2026.

Pay
  • This postingNot listed
  • Texas median$127–230K/yr214 salary ranges
  • US median$35–68/hr109 hourly ranges
Openings
Olive Jobs lists 281 open Machine Learning Engineer jobs in Texas and 6,196 across the US. 7% of them nationwide are listed as remote or flexible.
Hiring the most in Texas
Experience
This posting asks for at least 5 years of experience; Machine Learning Engineer postings that state a minimum ask for 5 years at the median (5,032 postings).
Education
2,074 of the 6,196 open Machine Learning Engineer jobs (33%) ask for a bachelor's degree or higher.

Counted across the open, public US postings on Olive Jobs on November 11, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.

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