Applied ML Engineer – AI/ML Evaluation & Simulation
Apple
Applied ML Engineer – AI/ML Evaluation & Simulation
Job Description
We’re building the next generation of AI evaluation systems — and we’re looking for a motivated early-career engineer who’s excited to work at the intersection of ML, software, and product. You’ll join a team focused on making AI systems — including LLMs and agentic AI - more measurable, testable, and trustworthy in real-world scenarios. This is a hands-on, collaborative role ideal for someone with a strong foundation in software engineering and machine learning, and an eagerness to grow by building tools and systems that help evaluate advanced AI behavior at scale. As an Applied ML Engineer on our team, you’ll help develop simulation systems, support data tooling, and contribute to evaluation workflows that improve the reliability of modern AI. You’ll collaborate closely with experienced engineers and researchers, learning how to instrument, monitor, and analyze model behavior — especially for language models and agent-style systems. This is a great opportunity for someone early in their career to work with cutting-edge AI technologies in a high-impact, supportive environment. You’ll gain experience working with large-scale ML systems, learn best practices in applied AI, and grow your skills across engineering, product, and research.
Locations
- Cupertino, California, United States 95014
- Seattle, Washington, United States 98117
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 50,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- software engineeringintermediate
- machine learningintermediate
- develop simulation systemsintermediate
- support data toolingintermediate
- evaluation workflowsintermediate
- instrument model behaviorintermediate
- monitor model behaviorintermediate
- analyze model behaviorintermediate
- large-scale ML systemsintermediate
- applied AIintermediate
- collaborativeintermediate
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field (degree in master)
- Strong programming skills in Python or another modern language (e.g., Java, Swift, (experience)
- Go) (experience)
- Basic understanding of machine learning principles (experience)
- Interest in LLMs, generative AI, or agent-based systems (experience)
- Curiosity about how to evaluate and improve real-world AI performance (experience)
- Strong collaboration and communication skills (experience)
Preferred Qualifications
- Coursework or internship experience in ML, AI systems, or applied data science (experience)
- Familiarity with training or evaluating models (even via coursework or personal (experience)
- projects) (experience)
- Exposure to tools like PyTorch, TensorFlow, or Hugging Face (experience)
- Interest in AI observability, behavior simulation, or synthetic data (experience)
- Passion for working cross-functionally in fast-moving, exploratory teams (experience)
Responsibilities
- As an Applied ML Engineer on our team, you’ll help develop simulation systems,
- support data tooling, and contribute to evaluation workflows that improve the reliability
- of modern AI. You’ll collaborate closely with experienced engineers and researchers,
- learning how to instrument, monitor, and analyze model behavior — especially for
- language models and agent-style systems.
- This is a great opportunity for someone early in their career to work with cutting-edge AI
- technologies in a high-impact, supportive environment. You’ll gain experience working
- with large-scale ML systems, learn best practices in applied AI, and grow your skills
- across engineering, product, and research.
- Contribute to systems that simulate interactive behaviors (including LLM-driven
- agents)
- Help build tools to support dataset generation and evaluation workflows
- Assist in developing pipelines for structured insights from model behavior
- Collaborate with teammates to debug and improve evaluation systems
- Write clean, testable code to support scalable and reliable infrastructure
- Learn how to define metrics that connect model behavior to real-world outcomes
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Answer 10 quick questions to check your fit for Applied ML Engineer – AI/ML Evaluation & Simulation @ Apple.
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Applied ML Engineer – AI/ML Evaluation & Simulation
Apple
Applied ML Engineer – AI/ML Evaluation & Simulation
Job Description
We’re building the next generation of AI evaluation systems — and we’re looking for a motivated early-career engineer who’s excited to work at the intersection of ML, software, and product. You’ll join a team focused on making AI systems — including LLMs and agentic AI - more measurable, testable, and trustworthy in real-world scenarios. This is a hands-on, collaborative role ideal for someone with a strong foundation in software engineering and machine learning, and an eagerness to grow by building tools and systems that help evaluate advanced AI behavior at scale. As an Applied ML Engineer on our team, you’ll help develop simulation systems, support data tooling, and contribute to evaluation workflows that improve the reliability of modern AI. You’ll collaborate closely with experienced engineers and researchers, learning how to instrument, monitor, and analyze model behavior — especially for language models and agent-style systems. This is a great opportunity for someone early in their career to work with cutting-edge AI technologies in a high-impact, supportive environment. You’ll gain experience working with large-scale ML systems, learn best practices in applied AI, and grow your skills across engineering, product, and research.
Locations
- Cupertino, California, United States 95014
- Seattle, Washington, United States 98117
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 50,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- software engineeringintermediate
- machine learningintermediate
- develop simulation systemsintermediate
- support data toolingintermediate
- evaluation workflowsintermediate
- instrument model behaviorintermediate
- monitor model behaviorintermediate
- analyze model behaviorintermediate
- large-scale ML systemsintermediate
- applied AIintermediate
- collaborativeintermediate
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field (degree in master)
- Strong programming skills in Python or another modern language (e.g., Java, Swift, (experience)
- Go) (experience)
- Basic understanding of machine learning principles (experience)
- Interest in LLMs, generative AI, or agent-based systems (experience)
- Curiosity about how to evaluate and improve real-world AI performance (experience)
- Strong collaboration and communication skills (experience)
Preferred Qualifications
- Coursework or internship experience in ML, AI systems, or applied data science (experience)
- Familiarity with training or evaluating models (even via coursework or personal (experience)
- projects) (experience)
- Exposure to tools like PyTorch, TensorFlow, or Hugging Face (experience)
- Interest in AI observability, behavior simulation, or synthetic data (experience)
- Passion for working cross-functionally in fast-moving, exploratory teams (experience)
Responsibilities
- As an Applied ML Engineer on our team, you’ll help develop simulation systems,
- support data tooling, and contribute to evaluation workflows that improve the reliability
- of modern AI. You’ll collaborate closely with experienced engineers and researchers,
- learning how to instrument, monitor, and analyze model behavior — especially for
- language models and agent-style systems.
- This is a great opportunity for someone early in their career to work with cutting-edge AI
- technologies in a high-impact, supportive environment. You’ll gain experience working
- with large-scale ML systems, learn best practices in applied AI, and grow your skills
- across engineering, product, and research.
- Contribute to systems that simulate interactive behaviors (including LLM-driven
- agents)
- Help build tools to support dataset generation and evaluation workflows
- Assist in developing pipelines for structured insights from model behavior
- Collaborate with teammates to debug and improve evaluation systems
- Write clean, testable code to support scalable and reliable infrastructure
- Learn how to define metrics that connect model behavior to real-world outcomes
Tags & Categories
Answer 10 quick questions to check your fit for Applied ML Engineer – AI/ML Evaluation & Simulation @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs