> Markdown version of [/jobs/ext/249931-machine-learning-engineer-vision-products-software](https://www.wearedevelopers.com/jobs/ext/249931-machine-learning-engineer-vision-products-software). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer - Vision Products Software

- **Company:** Apple Inc.
- **Location:** Los Angeles, CA, United States
- **Experience:** Expert
- **Salary:** $171,600.0 - $302,200.0
- **Contract:** Permanent contract
- **Skills:** Computer Animation, Artificial Intelligence, Data Analysis, Computer Vision, Python (Programming Language), Machine Learning, Natural Language Processing, Performance Tuning, Tensorflow, Systems Integration, Pytorch, Prompt Engineering, Deep Learning, Generative AI, Machine Learning Operations, Artificial Intelligence Markup Language (AIML)
- **Published:** May 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a79d1a2208c86524

## About the Role

Do you have experience in Systems integration?, Experience with conversational AI systems or interactive ML-driven experiences

Exposure to multimodal or embodied AI systems (voice, animation, agents, or real-time interaction)

Familiarity with Apple platforms, frameworks, or ML infrastructure

Evaluation based measurement expertise using eval frameworks to measure non-deterministic experiences for consumer facing products

Minimum Qualifications

Phd +3yrs or MS + 5yrs relevant experience in related field (AIML, CS, EE etc.)

Strong experience with deep learning, particularly in natural language processing and/or multimodal models

Hands-on experience with modern generative AI systems and computer vision models, including prompt design, fine-tuning, and evaluation

Familiarity with ranking, retrieval, and personalization algorithms

Proficiency in Python and experience with ML frameworks such as PyTorch (TensorFlow a plus)

Experience integrating ML models into production systems

Strong communication skills and the ability to collaborate across disciplines

Curiosity, pragmatism, and a product-oriented mindset

## Description

As a Machine Learning Engineer on our team, you will design, prototype, and deploy ML-driven features that power conversational and generative experiences. You'll work hands-on with modern model architectures; ranging from large language and multimodal models to ranking and personalization system and integrate them into production frameworks used at scale.

You'll collaborate closely with researchers, product managers, designers, and platform engineers to align on problem definitions, iterate on solutions, and ship features that meet Apple's standards for reliability, privacy, and delight. This role balances experimentation with execution: moving quickly when exploring new ideas, and rigorously when transitioning work into production.","responsibilities":"Design and implement ML models and algorithms for conversational understanding, generation, and personalization

Rapidly prototype and evaluate new model architectures, prompting strategies, and fine-tuning approaches

Translate research ideas into production-ready systems, collaborating across teams to ensure smooth integration

Contribute to end-to-end ML pipelines, from data exploration and model training to deployment and evaluation

Write clean, maintainable, and well-tested code that meets Apple's production standards

Participate in architecture discussions, design reviews, and peer code reviews

Help shape technical direction and contribute to roadmaps for next-generation AI capabilities.


## Related Videos

- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Edge AI on iOS: Beyond the Cloud, Designing the Next Generation of Intelligent On-Device Apps](https://www.wearedevelopers.com/videos/100225-edge-ai-on-ios-beyond-the-cloud-designing-the-next-generation-of-intelligent-on-device-apps)
- [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale)
- [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction)