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Ai Machine Learning Engineer Jobs in Goleta, CA (NOW HIRING)

AppFolio

Staff Machine Learning Engineer

Goleta, CA

Realm-X is AppFolio's AI-native platform powering this transformation. It enables a new generation ... Who We Are Looking For We're hiring a Staff Machine Learning Engineer to help move forward the ML ...

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.

Toyon

Deep Learning Algorithm Developer

Goleta, CA · On-site

$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 ...

Deckers

VP, Chief Data & Analytics Officer

Goleta, CA · Hybrid

Proficient in Agile methodologies, AI, machine learning, statistical analysis, programming, and data architecture What We'll Give You - * Competitive Pay and Bonuses - We've created a variety of ...

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All JobsAi Machine Learning Engineer JobsAi Machine Learning Engineer Jobs in Goleta, CA

Ai Machine Learning Engineer information

See Goleta, CA salary details

$34K

$138.9K

$208.8K

How much do ai machine learning engineer jobs pay per year?

As of Jul 26, 2026, the average yearly pay for ai 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 AI or machine learning engineer, research director, or executive role that offers a total compensation package including salary, bonuses, and stock options. These roles usually require advanced skills in deep learning, data science, and experience with tools like TensorFlow or PyTorch, often in competitive tech or finance industries. Such compensation reflects significant expertise, leadership responsibilities, and impact on strategic AI initiatives.

What are the key skills and qualifications needed to thrive as an AI Machine Learning Engineer, and why are they important?

To thrive as an AI Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (often Python or R), and a relevant degree such as computer science or engineering. Familiarity with frameworks like TensorFlow, PyTorch, and scikit-learn, as well as experience with cloud platforms and data processing tools, is highly valued, along with certifications in AI or machine learning. Critical thinking, problem-solving, and effective communication are essential soft skills for collaborating with teams and translating business needs into technical solutions. These competencies are crucial for developing accurate, scalable AI models that deliver real-world value and drive innovation.

What are some common challenges that AI Machine Learning Engineers face when deploying models to production environments?

AI Machine Learning Engineers often encounter challenges such as ensuring model scalability, managing data pipeline reliability, and handling model drift once solutions are live. They also need to collaborate closely with DevOps and software engineering teams to integrate models seamlessly into existing systems, while maintaining performance and security. Addressing these challenges requires a strong understanding of both machine learning principles and software deployment best practices.

What engineers make $500,000?

Senior AI Machine Learning Engineers with extensive experience, advanced skills in deep learning, and proficiency in tools like TensorFlow or PyTorch can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a strong track record, specialized certifications, and leadership responsibilities.

Is AI ML engineer in demand?

AI and ML engineers are in high demand across various industries due to the increasing adoption of artificial intelligence technologies. Companies seek professionals skilled in programming, data analysis, and machine learning frameworks like TensorFlow and PyTorch to develop and deploy AI solutions, leading to strong job growth and competitive salaries in this field.

Which 3 jobs will survive AI?

AI Machine Learning Engineers are likely to continue to be in demand because they develop and refine AI models, requiring specialized skills in programming, data analysis, and domain knowledge. Jobs that involve complex problem-solving, creativity, and emotional intelligence, such as healthcare professionals, educators, and skilled trades, are also expected to persist alongside AI advancements. These roles often require human judgment and adaptability that AI cannot fully replicate.

What is an AI Machine Learning Engineer?

An AI Machine Learning Engineer is a professional who designs, builds, and deploys artificial intelligence and machine learning models to solve real-world problems. They work with large datasets, select appropriate algorithms, and optimize models for accuracy and efficiency. Their role often involves both software engineering and data science skills, and they collaborate with other teams to integrate these models into products or services. AI Machine Learning Engineers are in high demand across industries such as technology, healthcare, finance, and more.

What is the difference between Ai Machine Learning Engineer vs Data Scientist?

AspectAi Machine Learning EngineerData Scientist
CredentialsDegree in CS, AI, or related fields; certifications in ML frameworksDegree in CS, Statistics, or related fields; certifications in data analysis
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, where deploying ML models is keyResearch, business intelligence, analytics across industries

While both roles involve working with data and machine learning, Ai Machine Learning Engineers focus on building and deploying scalable ML models in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core focus and responsibilities.

What are popular job titles related to Ai Machine Learning Engineer jobs in Goleta, CA? For Ai Machine Learning Engineer jobs in Goleta, CA, the most frequently searched job titles are:
What job categories do people searching Ai Machine Learning Engineer jobs in Goleta, CA look for? The top searched job categories for Ai Machine Learning Engineer jobs in Goleta, CA are:
Ai Machine Learning Engineer jobs near you
Infographic showing various Ai Machine Learning Engineer job openings in Goleta, CA as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $138,937 per year, or $66.8 per hour.

Machine Learning Engineer

Quantum Machines

Santa Barbara, CA • On-site

Full-time

Posted 27 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

Apply