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Contract Apple Machine Learning Engineer Jobs in Goleta, CA

AppFolio

Staff Machine Learning Engineer

Goleta, CA

Who We Are Looking For We're hiring a Staff Machine Learning Engineer to help move forward the ML platform that every AI initiative at AppFolio depends on -- training, fine-tuning, inference, RAG ...

Toyon

Deep Learning Algorithm Developer

Goleta, CA

$120K - $200K/yr

Toyon has openings for researchers and developers to solve challenging real-world problems using Artificial Intelligence (AI) / Machine Learning (ML) techniques. Experience in Computer Vision is ...

Toyon

AI/ML Software Engineer

Goleta, CA

$120K - $200K/yr

Toyon is seeking highly qualified AI/ML Software Engineer candidates to develop software in the Python or C++ languages in support of Artificial Intelligence (AI) / Machine Learning (ML) applications.

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All JobsContract Apple Machine Learning Engineer JobsContract Apple Machine Learning Engineer Jobs in Goleta, CA

Contract Apple Machine Learning Engineer information

See Goleta, CA salary details

$34K

$138.9K

$208.8K

How much do contract apple machine learning engineer jobs pay per year?

As of Jul 26, 2026, the average yearly pay for contract apple machine learning engineer in Goleta, CA is $138,937.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,500.00 and $167,200.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position such as a senior machine learning engineer or AI research scientist, often in leading tech companies. These roles usually require advanced skills in deep learning, data analysis, and experience with tools like TensorFlow or PyTorch, and may include performance-based bonuses or stock options that contribute to the total compensation.

What are the key skills and qualifications needed to thrive as a Contract Apple Machine Learning Engineer, and why are they important?

To thrive as a Contract Apple Machine Learning Engineer, you need a strong background in computer science, mathematics, and deep learning, typically with a relevant degree and experience in building ML models. Proficiency with Python, TensorFlow or PyTorch, Apple's Core ML framework, and version control systems is commonly required. Strong problem-solving skills, collaboration, and effective communication help you navigate project requirements and work with cross-functional teams. These skills and experiences are crucial for delivering high-quality, scalable machine learning solutions that align with Apple's standards and rapidly evolving technology needs.

Which 3 jobs will survive AI?

For a Contract Apple Machine Learning Engineer, roles that require complex problem-solving, creativity, and human interaction—such as AI ethics specialists, creative designers, and strategic consultants—are less likely to be fully replaced by AI. These jobs often involve nuanced judgment, emotional intelligence, and domain expertise that AI cannot replicate easily.

Will MLE be replaced by AI?

As a Contract Apple Machine Learning Engineer, the role involves designing and implementing ML models, which AI systems currently support but do not fully replace. Human expertise in model development, data analysis, and system integration remains essential, especially for complex tasks and ethical considerations. AI tools can augment the work of MLEs but are unlikely to fully replace the need for skilled engineers in the near future.

How much does Apple pay machine learning engineers?

Apple pays machine learning engineers an average salary ranging from $120,000 to $180,000 annually, depending on experience, location, and level. Compensation may also include bonuses, stock options, and benefits, with roles often requiring expertise in deep learning, data analysis, and programming in Python or Swift.

What are the common challenges faced by Contract Apple Machine Learning Engineers when integrating ML models into Apple’s ecosystem?

Contract Apple Machine Learning Engineers often encounter challenges such as ensuring seamless integration of machine learning models with Apple’s proprietary platforms like iOS, macOS, or Core ML. Adapting to Apple’s strict security, privacy standards, and performance requirements is essential, as is optimizing models for real-time performance on Apple devices. Collaborating effectively with cross-functional teams—such as software developers, designers, and QA engineers—is crucial to deliver scalable and user-friendly ML features within project timelines.

What are Contract Apple Machine Learning Engineers?

Contract Apple Machine Learning Engineers are professionals hired on a temporary or project basis to develop and implement machine learning models and algorithms specifically for Apple’s products and platforms. They typically work on tasks such as optimizing machine learning workflows for iOS, macOS, or other Apple technologies, and may collaborate closely with Apple’s in-house teams. Their responsibilities can include data preprocessing, model training, evaluation, and integration into Apple’s ecosystem. These engineers are expected to have expertise in machine learning frameworks, programming languages like Python or Swift, and a strong understanding of Apple’s development tools. Contract roles often provide flexibility but may require quick adaptation to Apple’s proprietary systems and high standards.
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Contract Apple Machine Learning Engineer jobs near you

Machine Learning Engineer

Quantum Machines

Santa Barbara, CA • On-site

Full-time

Posted 28 days ago

Job description

Description
Quantum Machines (QM) is a global leader in quantum computing control systems. Through our pioneering hardware and software solutions based on instruction-based quantum control, we're revolutionizing how quantum computers are built and controlled. As we stand at the forefront of exponential growth in quantum computing, we're assembling an elite team that actively shapes the evolution of quantum technologies.
We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions that run on real hardware in production labs.
You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks that make quantum control more autonomous, more sample-efficient, and more robust to drift. This position offers unprecedented exposure to diverse qubit types and quantum architectures, with a tight feedback loop between your models and the systems they steer, and the opportunity to deliver groundbreaking ML-driven solutions to the labs and companies defining the next generation of quantum systems.
Responsibilities:
  • Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement, to be deployed on real hardware.
  • Develop real-time parameter steering for calibration during QEC and between circuits.
  • Develop and maintain agentic frameworks for autonomous system control and calibration.
  • Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum Machines control stack, including QUA, Qualibrate, and the OPX1000.
  • Work directly with customers and partner labs to deploy, validate, and iterate on ML solutions in real experimental environments.
  • Collaborate cross-functionally with product, R&D, and hardware teams, contributing to internal libraries, customer-facing SDKs, and training materials.

Requirements
  • PhD/Master in Machine Learning, Physics, Applied Physics, Quantum Information Science, or a related field. 4+ years of relevant experience
  • Strong background in Machine Learning and Deep Learning, with hands-on experience in at least one of: deep learning, reinforcement learning, agentic AI
  • Strong Python proficiency, including scientific or systems-oriented codebases
  • Solid software engineering fundamentals (architecture, Git workflows, testing, code review)
  • Proven track record of taking ML from prototype to deployment under real-world constraints - non-stationary data, expensive evaluations, or safety-critical action spaces. Robotics, online control, autonomous vehicles, or hardware-in-the-loop ML all transfer well
  • Strong problem-solving skills and customer-focused mindset; ability to work independently and in multidisciplinary teams
  • Proven software development track record and excellent technical communication skills
  • Familiarity with quantum computing concepts - qubit calibration, randomized benchmarking, QEC, optimal control- advantage
  • Experience with sim-to-real, multi-objective RL, or meta-learning- advantage

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