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Junior Machine Learning Engineer Jobs in Santa Barbara, CA

AppFolio

Sr. Machine Learning Engineer

Goleta, CA

$112K - $154K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

AppFolio, Inc.

Sr. Machine Learning Engineer

Santa Barbara, CA · On-site +1

$116K - $159K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

AppFolio

Sr. Machine Learning Engineer

Santa Barbara, CA · On-site

$112K - $154K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

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 ...

AppFolio

Staff Machine Learning Engineer - Leasing

Goleta, CA · On-site

$18.25 - $21.50/hr

Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously ...

Toyon

Deep Learning Algorithm Developer

Goleta, CA · On-site

$120K - $200K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 Research Corporation

AI/ML Software Engineer

Goleta, CA · On-site

$120K - $200K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Santa Barbara, CA

Junior Machine Learning Engineer information

See Santa Barbara, CA salary details

$37.3K

$79.9K

$121.8K

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

As of Aug 19, 2026, the average yearly pay for junior machine learning engineer in Santa Barbara, CA is $79,890.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,000.00 and $89,000.00 per year, depending on experience, location, and employer.

What does a junior machine learning engineer do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

What kinds of projects and responsibilities can a junior machine learning engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

What are the key skills and qualifications needed to thrive as a junior machine learning engineer, and why are they important?

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

How much do junior machine learning engineers make?

Junior machine learning engineers typically earn between $70,000 and $100,000 annually, depending on location, education, and industry. Entry-level roles often require knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
More about Junior Machine Learning Engineer jobs

What are the most commonly searched types of Machine Learning Engineer jobs in Santa Barbara, CA?

The most popular types of Machine Learning Engineer jobs in Santa Barbara, CA are:

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For Junior Machine Learning Engineer jobs in Santa Barbara, CA, the most frequently searched job titles are:

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The top searched job categories for Junior Machine Learning Engineer jobs in Santa Barbara, CA are:

What cities near Santa Barbara, CA are hiring for Junior Machine Learning Engineer jobs?

Cities near Santa Barbara, CA with the most Junior Machine Learning Engineer job openings:

Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Santa Barbara, CA as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $79,890 per year, or $38.4 per hour.

Staff Machine Learning Engineer

AppFolio, Inc

Santa Barbara, CA • On-site

$200 - $250/hr

Other

Re-posted 18 days ago

AppFolio rating

7.2

Company rating: 7.2 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

185th of 245 rated software companies

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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