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Machine Learning Engineer New Grad Jobs in Santa Barbara, CA

AppFolio

Sr. Machine Learning Engineer

Goleta, CA

$112K - $154K/yr

It enables a new generation of intelligent capabilities across our products, including Realm-X ... 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

It enables a new generation of intelligent capabilities across our products, including Realm-X ... 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

It enables a new generation of intelligent capabilities across our products, including Realm-X ... Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next ...

AppFolio

Staff Machine Learning Engineer

Goleta, CA

It enables a new generation of intelligent capabilities across our products, including Realm-X ... Who We Are Looking For We're hiring a Staff Machine Learning Engineer to help move forward the ML ...

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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Machine Learning Engineer New Grad information

See Santa Barbara, CA salary details

$35K

$143.3K

$215.3K

How much do machine learning engineer new grad jobs pay per year?

As of Aug 26, 2026, the average yearly pay for machine learning engineer new grad in Santa Barbara, CA is $143,278.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,900.00 and $172,500.00 per year, depending on experience, location, and employer.

What is a machine learning engineer new grad?

A Machine Learning Engineer New Grad job is an entry-level role for recent graduates specializing in machine learning and artificial intelligence. It typically involves developing, training, and deploying machine learning models, working with large datasets, and optimizing algorithms for performance. New grads in this role often collaborate with data scientists, software engineers, and product teams to integrate models into applications. Employers look for proficiency in programming (Python, TensorFlow, PyTorch), a strong foundation in ML concepts, and experience with data processing. This role provides an opportunity to gain hands-on industry experience and grow technical skills in real-world applications.

What are the typical day-to-day tasks of a machine learning engineer new grad?

As a Machine Learning Engineer New Grad, your daily tasks often include collecting and preprocessing data, developing and testing machine learning models, and analyzing model performance. You may work closely with data scientists and software engineers to integrate models into production systems and address real-world business problems. Participating in team meetings, code reviews, and collaborative projects is common, providing opportunities to learn best practices and receive mentorship. This hands-on, varied workload helps you quickly build technical and collaborative skills early in your career.

What are the key skills and qualifications needed to thrive in the machine learning engineer new grad position, and why are they important?

To thrive as a Machine Learning Engineer New Grad, a strong background in computer science, statistics, and mathematics, often supported by a relevant degree, is essential. Familiarity with programming languages like Python or Java, machine learning frameworks (such as TensorFlow or PyTorch), and basic knowledge of data tools and cloud platforms is typically required. Effective problem-solving, eagerness to learn, and clear communication help new grads excel when collaborating on projects and learning from senior team members. These skills and qualities are vital for adapting quickly, contributing to team goals, and building a successful foundation in this fast-evolving technical field.

What are popular job titles related to Machine Learning Engineer New Grad jobs in Santa Barbara, CA?

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

What job categories do people searching Machine Learning Engineer New Grad jobs in Santa Barbara, CA look for?

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

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

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

Machine Learning Engineer New Grad jobs near you
Infographic showing various Machine Learning Engineer New Grad 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 $143,278 per year, or $68.9 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

184th of 246 rated software companies

Good employer

Respectful managers

Learn new skills

Learn more about AppFolio, Incexternal

$200 - $250/hr

Other

Re-posted 25 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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