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Afternoon Full Stack Machine Learning Engineer Jobs in Goleta, CA

AppFolio

Staff Machine Learning Engineer

Goleta, CA

... full context of property management workflows. This foundation allows us to build context-aware ... Training & Fine-Tuning Stack: Build the training and fine-tuning stack for Small Language Models ...

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 ...

AppFolio

Sr. Machine Learning Engineer

Santa Barbara, CA

$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 ...

Unwrap

Full Stack Engineer

Santa Barbara, CA · On-site

Unwrap is a company on a mission to fill the world with products people love, and they are seeking a full stack software engineer. The role involves building customer-facing components across the ...

AppFolio

Staff Machine Learning Engineer

Santa Barbara, CA · On-site

$200 - $250/hr

Build the training and fine‑tuning stack for small language models, including data pipelines, GPU orchestration, and evaluation. * Productionize research prototypes with SLOs, on‑call rotations ...

Unwrap

Senior Full Stack Engineer

Santa Barbara, CA · On-site

Job Summary : Unwrap is a company focused on building products that people love, and they are seeking a Senior Full Stack Engineer to work across the tech stack on both frontend and backend ...

Worky

Senior Full-Stack Software Engineer

Goleta, CA · On-site

$87 - $141/hr

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 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 ...

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Afternoon Full Stack Machine Learning Engineer information

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$48K

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

As of Aug 22, 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.

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Staff Machine Learning Engineer

AppFolio, Inc

Santa Barbara, CA • On-site

$200 - $250/hr

Other

Re-posted 21 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 246 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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