ZipRecruiter

Log In

1

Deep Learning Engineer Jobs in Colorado (NOW HIRING)

AppFolio

CO · On-site

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 ...

AppFolio

CO · On-site

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 ...

Blissway Inc.

Machine Learning Engineer

Denver, CO · On-site

Deep Tech, AI/ML, Hardware, SaaS, and IoT. For the past five years, we have built a nearly ... Machine Learning Engineer The Mission: You are the engineer who ships the model, not just the one ...

Blissway Inc

Machine Learning Engineer

Denver, CO · On-site

$145K - $195K/yr

Deep Tech, AI/ML, Hardware, SaaS, and IoT. For the past five years, we have built a nearly ... Machine Learning Engineer The Mission: You are the engineer who ships the model, not just the one ...

Blissway Inc

Machine Learning Engineer

Denver, CO · On-site

$145K - $195K/yr

Deep Tech, AI/ML, Hardware, SaaS, and IoT. For the past five years, we have built a nearly ... Machine Learning Engineer The Mission: You are the engineer who ships the model, not just the one ...

previous page

Showing results 41-60

All JobsDeep Learning Engineer JobsDeep Learning Engineer Jobs in Colorado

Deep Learning Engineer information

See Colorado salary details

$40K

$121.8K

$201.4K

How much do deep learning engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for deep learning engineer in Colorado is $121,834.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,300.00 and $159,300.00 per year, depending on experience, location, and employer.

What is a deep learning engineer?

A Deep Learning Engineer is a specialized software engineer who designs, develops, and optimizes deep learning models. They work with neural networks, large datasets, and frameworks like TensorFlow or PyTorch to build AI systems for tasks like image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, performance tuning, and deploying models into production. Strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration are essential for this role.

What does a deep learning engineer do?

Deep Learning Engineers typically spend their days designing, developing, and optimizing neural network models for tasks like image recognition, natural language processing, or recommendation systems. They preprocess and analyze large datasets, experiment with model architectures, and tune hyperparameters to achieve the best performance. Collaboration is often required with data scientists, product managers, and software engineers to integrate models into real-world applications and scale solutions for production. Additionally, many deep learning engineers review current research, stay updated on advancements in AI, and continuously improve their skills. This role offers a dynamic work environment where learning and innovation are highly encouraged.

What skills and qualifications does a deep learning engineer need?

To thrive as a Deep Learning Engineer, you need a strong background in mathematics, machine learning theory, and programming (especially Python), often supported by a relevant degree in computer science, engineering, or related fields. Proficiency with frameworks such as TensorFlow, PyTorch, Keras, as well as experience with GPUs and cloud platforms, is highly valued, and certifications in AI or deep learning can further enhance your profile. Effective problem-solving, strong collaboration skills, and clear communication are important soft skills for excelling in interdisciplinary teams. These abilities ensure that you can develop robust deep learning models, adapt to evolving technologies, and contribute value in both technical and collaborative settings.

Are deep learning engineers in demand?

Deep learning engineers are in high demand due to the growth of artificial intelligence and machine learning applications across industries such as technology, healthcare, and finance. They typically require skills in neural networks, programming languages like Python, and frameworks such as TensorFlow or PyTorch, with job opportunities increasing as AI adoption expands.

What are the most commonly searched types of Deep Learning Engineer jobs in Colorado?

The most popular types of Deep Learning Engineer jobs in Colorado are:

What are popular job titles related to Deep Learning Engineer jobs in Colorado?

For Deep Learning Engineer jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Deep Learning Engineer jobs in Colorado look for?

The top searched job categories for Deep Learning Engineer jobs in Colorado are:

What cities in Colorado are hiring for Deep Learning Engineer jobs?

Cities in Colorado with the most Deep Learning Engineer job openings:

Deep Learning Engineer jobs near you
Infographic showing various Deep Learning Engineer job openings in Colorado as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $121,834 per year, or $58.6 per hour.

Staff Machine Learning Engineer

CO • On-site

AppFolio
See more jobs at AppFolioexternal

Full-time

Re-posted 10 days ago

AppFolio rating

7.2

Company rating: 7.2 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

184th of 247 rated software companies

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
Find out more about our locations by visiting our s

What AppFolio employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply