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Afternoon Full Stack Machine Learning Engineer Jobs in Goleta, CA

AppFolio

Sr. Machine Learning Engineer

Santa Barbara, CA · On-site

$112K - $154K/yr

... full context of property management workflows. This foundation allows us to build context-aware ... Voice stack: Hands-on with Voice-to-Voice models and traditional TTS / STT pipelines; understands ...

AppFolio

Sr. Machine Learning Engineer

Santa Barbara, CA · On-site

$112K - $154K/yr

... full context of property management workflows. This foundation allows us to build context-aware ... Voice stack: Hands-on with Voice-to-Voice models and traditional TTS / STT pipelines; understands ...

AppFolio, Inc.

Sr. Machine Learning Engineer

Santa Barbara, CA · On-site +1

$116K - $159K/yr

... full context of property management workflows. This foundation allows us to build context-aware ... Voice stack: Hands-on with Voice-to-Voice models and traditional TTS / STT pipelines; understands ...

Worky

Senior Full-Stack Software Engineer

Goleta, CA · On-site

We are looking for an experienced Full-Stack Software Engineer to join our software team and help build reliable, scalable, and production-ready platforms. In this role, you will work across the ...

Worky

Full-Stack Software Engineer

Goleta, CA · On-site

We are looking for an experienced Full-Stack Software Engineer to join our software team and help build reliable, scalable, and production-ready platforms. In this role, you will work across the ...

Tecolote

AI Developer

Goleta, CA · On-site

The position is focused on production-quality full-stack software engineering and cloud deployment of AI-enabled applications rather than AI research or experimentation. Responsibilities The ideal ...

Tecolote Research

AI Developer

Goleta, CA · On-site

We are looking for a hands-on full-stack software engineer who specializes in building AI-enabled applications and can work across an Angular front end, Python back-end services, APIs, and the AWS ...

Tecolote Research, Inc.

AI Developer

Goleta, CA · On-site

We are looking for a hands-on full-stack software engineer who specializes in building AI-enabled applications and can work across an Angular front end, Python back-end services, APIs, and the AWS ...

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All JobsAfternoon Full Stack Machine Learning Engineer JobsAfternoon Full Stack Machine Learning Engineer Jobs in Goleta, CA

Afternoon Full Stack Machine Learning Engineer information

See Goleta, CA salary details

$48K

$145.4K

$205.5K

How much do afternoon full stack machine learning engineer jobs pay per year?

As of Sep 13, 2026, the average yearly pay for afternoon full stack machine learning engineer in Goleta, CA is $145,412.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,800.00 and $170,500.00 per year, depending on experience, location, and employer.

What job categories do people searching Afternoon Full Stack Machine Learning Engineer jobs in Goleta, CA look for?

The top searched job categories for Afternoon Full Stack Machine Learning Engineer jobs in Goleta, CA are:

Afternoon Full Stack Machine Learning Engineer jobs near you

Staff Machine Learning Engineer

Santa Barbara, CA • On-site

AppFolio
See more jobs at AppFolioexternal

Full-time

Re-posted 14 days ago

AppFolio rating

7.2

Company rating: 7.2 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Job description

Hi, We're AppFolio
We're innovators, changemakers, and collaborators. We're more than just a software company — we're building the AI-native platform where the real estate industry comes to do business. We're transforming Property Management; how property managers operate, how residents live, and how intelligence flows across an entire industry.
Realm-X is AppFolio's AI-native platform powering this transformation. It enables a new generation of intelligent capabilities across our products, including Realm-X Assistant (copilot), Flows (AI Agentic workflows) and Performers (autonomous AI Agents). Realm-X serves as both a foundation for internal teams to build and scale AI-powered products, and a core layer delivering intelligent, high-impact experiences directly to our customers.
At its core, Realm-X is built on a structured domain ontology and a set of shared business primitives—such as transactions, actions, reports, metrics, and skills—that enable AI systems to deeply understand and operate across the full context of property management workflows. This foundation allows us to build context-aware, action-oriented AI systems that go beyond simple assistance to power real automation and decision-making.
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, evaluation, and cost. You'll keep our AI cloud always-on, observable, and economical, while staying close enough to applications to influence model and agent design.
This role works at the intersection of ML infrastructure, applied AI, and cost discipline. You'll partner closely with our Voice & Agents and Research ML engineers to harden their prototypes into production systems, and help move forward the platform layer that lets Realm-X scale across AppFolio's entire customer base.
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 their 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: You think in terms of platforms and long-term leverage, not just features.
  • Production builder: You've built and scaled ML infrastructure in production with meaningful business impact.
  • Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction.
  • Owner-operator: You take ownership with a founder/owner-operator mindset, act with urgency, and focus on outcomes.
  • Pace: You have a strong desire to move fast and deliver impact, while maintaining sound engineering judgment.
  • Collaboration: You are humble, collaborative, and low-ego, and you elevate those around you.
  • Sustainability: You value work-life balance as a foundation for sustained high performance.
  • Reliability mindset: You treat ML infra like any other production system — SLOs, on-call, observability, postmortems.
Must Have
  • ML infra at scale: Has 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: Has 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.
Location
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What AppFolio employees say

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Hours and flexibility

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