Machine Learning Visa Sponsorship Jobs in Colorado
Colorado's machine learning job market centers on Denver and Boulder, where companies like Google, Lockheed Martin, and a dense cluster of healthtech and aerospace firms actively hire ML engineers and data scientists. The state's proximity to top research universities, including CU Boulder and Colorado State, creates a steady pipeline of technical talent and employers experienced with visa sponsorship.
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Get Access To All JobsINTRODUCTION
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.
ROLE AND RESPONSIBILITIES
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.
BASIC 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 site.
COMPENSATION & BENEFITS
The compensation that we reasonably expect to pay for this role is: $200,000 - 250,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate’s skills, education, experience, and internal equity.
Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type.
Regular full-time employees are eligible for benefits - see here.
INTRODUCTION
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.
ROLE AND RESPONSIBILITIES
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.
BASIC 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 site.
COMPENSATION & BENEFITS
The compensation that we reasonably expect to pay for this role is: $200,000 - 250,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate’s skills, education, experience, and internal equity.
Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type.
Regular full-time employees are eligible for benefits - see here.
Machine Learning Job Roles in Colorado
See all 41+ Machine Learning Jobs in Colorado
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Search Machine Learning Jobs in ColoradoMachine Learning Jobs in Colorado: Frequently Asked Questions
Which companies sponsor visas for machine learning roles in Colorado?
Several large employers in Colorado have consistent H-1B visa sponsorship histories for machine learning roles. Google's Boulder engineering office, Lockheed Martin, Palantir (which has significant Denver presence), and healthtech companies like DaVita and Optum are among those that have filed LCAs for ML and AI positions. Aerospace and defense contractors along the Front Range also recruit ML engineers and have established immigration processes.
Which visa types are most common for machine learning roles in Colorado?
The H-1B is the most common visa for machine learning engineers and data scientists in Colorado, as ML roles typically satisfy the specialty occupation requirement given the degree-level technical skills involved. Australians may pursue the E-3 visa as an alternative with no lottery. Candidates with exceptional research records or published work may also qualify for the O-1A. TN visa status is available to Canadian and Mexican nationals in qualifying computer-related occupations.
Which cities in Colorado have the most machine learning sponsorship jobs?
Denver and Boulder account for the majority of machine learning visa sponsorship activity in Colorado. Boulder's tech corridor hosts a concentration of AI startups and research-driven companies, partly due to CU Boulder's strong computer science programs. Denver attracts larger enterprise employers across healthtech, fintech, and aerospace. Colorado Springs has a smaller but active market tied to defense and government contracting, where ML roles in computer vision and data analysis appear regularly.
How to find machine learning visa sponsorship jobs in Colorado?
Migrate Mate is built specifically for international job seekers and filters machine learning roles in Colorado by visa sponsorship eligibility, saving you from manually screening hundreds of listings. You can browse current openings from employers who have demonstrated sponsorship history for ML and AI positions across Denver, Boulder, and the broader Front Range. Filtering by state and role on Migrate Mate surfaces opportunities that match both your technical background and immigration needs.
Are there state-specific factors that affect machine learning visa sponsorship in Colorado?
Colorado's Equal Pay for Equal Work Act requires employers to include compensation ranges in job postings, which gives machine learning candidates useful context when evaluating whether a role meets prevailing wage requirements under H-1B rules. The state's strong university pipeline from CU Boulder and Colorado School of Mines means employers here are generally familiar with OPT and STEM OPT extensions, making the transition from student status to sponsored employment more straightforward at many local companies.
What is the prevailing wage for sponsored machine learning jobs in Colorado?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.