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

AppFolio

Sr. Machine Learning Engineer

Santa Barbara, CA

$112K - $154K/yr

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 Senior Machine Learning Engineer to design and ship the next ...

AppFolio

Sr. Machine Learning Engineer

Santa Barbara, CA

$112K - $154K/yr

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 Senior Machine Learning Engineer to design and ship the next ...

AppFolio, Inc.

Sr. Machine Learning Engineer

Santa Barbara, CA · On-site +1

$116K - $159K/yr

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 Senior Machine Learning Engineer to design and ship the next ...

Toyon

AI/ML Software Engineer

Goleta, CA · On-site

$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 ...

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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 Aug 30, 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 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 are the key skills and qualifications needed to thrive as an AI machine learning engineer?

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 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.

Is AI Machine Learning Engineer in demand?

AI Machine Learning Engineers are in high demand due to the growing adoption of artificial intelligence across industries. They typically require skills in programming, data analysis, and familiarity with tools like TensorFlow or PyTorch, and job opportunities are expected to continue expanding as AI applications become more widespread.

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 August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 27% Part Time, 1% Temporary, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $138,937 per year, or $66.8 per hour.

Staff Machine Learning Engineer

Santa Barbara, CA • On-site

AppFolio, Inc

7.2

Company rating: 7.2 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

185th of 246 rated software companies

Good employer

Respectful managers

Learn new skills

Learn more about AppFolio, Incexternal

$200 - $250/hr

Other

Re-posted 29 days ago

Job description

Staff Machine Learning Engineer – Software Engineering

Locations: Santa Barbara, CA; San Diego, CA; Remote - San Francisco, CA; Remote - Denver, CO.

Overview

We’re building an AI‑native platform for the real estate industry and are looking for a Staff Machine Learning Engineer to advance the ML platform that underpins all of AppFolio’s AI initiatives.

Your Impact
  • ML Platform: Design and operate AppFolio’s ML infrastructure on AWS – ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls.
  • Drive AI Cost Discipline: Optimize cost across all AI applications – provider routing, caching, batch vs. real‑time, model-size selection, and inference economics.
  • Multi‑Provider Reliability: Maintain reliable, multi‑provider LLM access across Google, OpenAI, and Anthropic with sensible fallbacks and abstractions.
  • Training & Fine‑Tuning Stack: Build the training and fine‑tuning stack for small language models, including data pipelines, GPU orchestration, and evaluation.
  • Productionize Research: Partner with Voice & Agents and Research ML engineers to harden prototypes into production systems with SLOs, on‑call rotations, and observability.
  • AI Safety & Guardrails: Operate AppFolio’s AI safety and authorization layer – guardrails on AWS, scoped tool permissions, and human‑in‑the‑loop gates for autonomous agent actions.
Qualifications
  • Systems thinker: Think in terms of platforms and long‑term leverage, not just features.
  • Production builder: Built and scaled ML infrastructure in production with meaningful business impact.
  • Ambiguity: Operate effectively in high ambiguity, turning unclear infra problems into clear direction.
  • Owner‑operator: Take ownership with a founder/owner‑operator mindset, act with urgency, and focus on outcomes.
  • Pace: Strong desire to move fast and deliver impact while maintaining sound engineering judgment.
  • Collaboration: Humble, collaborative, low‑ego, and elevate those around you.
  • Sustainability: Value work‑life balance as a foundation for sustained high performance.
  • Reliability mindset: Treat ML infra like any other production system – SLOs, on‑call, observability, postmortems.
Must Have
  • ML infra at scale: Built and operated production ML infrastructure on AWS – ECS, SageMaker, GPUs, autoscaling, and cost controls.
  • Inference platforms: Production experience with model serving for both LLMs and custom models; understands quantization, batching, and routing.
  • Provider breadth: Direct experience integrating with Google (Vertex/Gemini), OpenAI, and Anthropic APIs in production.
  • Training capability: Trained or fine‑tuned language models end‑to‑end; comfortable with deep learning, evaluation, and inference.
  • Cloud‑native engineering: Strong Python, Docker, dependency management, and CI/CD for AI workloads.
  • RAG & agents: Working knowledge of LangChain / LangGraph and modern RAG patterns over structured and unstructured data.
  • Cost optimization: Demonstrated experience reducing unit cost of AI workloads without regressing quality or latency.
  • AI safety & authorization: Hands‑on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems.
Nice to Have
  • Experience training small language models for production use.
  • GPU performance tuning (vLLM, TensorRT, Triton, or similar).
  • Prior staff‑level role at a company with a significant AI infra footprint.
  • Experience with ontology‑driven systems or knowledge graphs supporting AI applications.
  • Contributions to open‑source ML infrastructure or LLM tooling.
Compensation & Benefits
  • Base pay range: $200,000 – $250,000. Additional benefits and bonuses may apply.
  • Regular full‑time employees are eligible for benefits.
Statement of Equal Opportunity

At AppFolio, we value diversity in backgrounds and perspectives. We are a proud Equal Opportunity Employer and welcome applicants of all races, colors, religions, sexes, sexual orientations, gender identifications, national origins, ages, marital statuses, ancestries, physical or mental disabilities, or veteran status.

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