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Scientific Machine Learning Jobs in Colorado (NOW HIRING)

Crowe LLP

Machine Learning Intern

Denver, CO · On-site

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

Crowe LLP

Machine Learning Intern

Denver, CO · On-site

$27 - $42/hr

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Collaborate closely with product managers, data scientists, and backend engineers to deeply ...

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All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Colorado

Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Colorado?

For Scientific Machine Learning jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Colorado look for?

The top searched job categories for Scientific Machine Learning jobs in Colorado are:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Colorado as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Principal Machine Learning Engineer

AppFolio

Denver, CO • On-site

Full-time

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

184th of 245 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 seeking a Principal Machine Learning Engineer to help define and lead the next generation of AI systems within Realm-X, and to drive AppFolio's long-term autonomous Real Estate Performance Management (RPM) platform — autonomous AI agents that can deliver property management performance.
This is a company-impact role. You will own mission-critical AI capabilities, shape long-term technical strategy, and act as a technical visionary and advisor across teams and leadership.
You'll operate at the intersection of traditional machine learning, deep learning, and generative AI, building systems that go beyond AI assistance into execution, automation, and optimization.
This role is for someone who doesn't just build systems — but redefines how they should be built.
Your Impact
  • Architect & Lead: Help define the technical vision and architecture for AI systems across Realm-X in partnership with senior leadership.
  • Scale Intelligent AI Agents: Design and deploy advanced AI Agentic systems that combine reasoning, planning, and execution, including multi-agent orchestration across specialist agents (e.g., maintenance, leasing, accounting, collections).
  • Improve the Foundation: Establish platform primitives and abstractions to enable context-aware, action-oriented AI that goes beyond simple assistance to true automation. Improve the standards for end-to-end ML systems: data collection, model training, evaluation, deployment, and inference infrastructure.
  • Production Excellence: Architect and build scalable, multi-modal, and real-time AI applications, ensuring high-quality deployment standards.
  • ML for Autonomous Property Management: Drive AppFolio's transition toward autonomous property management operations. Use existing LLMs today and instrument the proprietary data collection now that will let us selectively train, fine-tune, and RL-optimize open source LLM and SLM for the RPM domain — optimizing performance, latency, and cost.
  • Reinforcement Learning for Agent Policies: Build the data and feedback loops needed to enable Reinforcement Learning over agent action policies in the partially observable, high-stakes property management environment.
Qualifications
  • Systems thinker: You think in terms of systems, platforms, and long-term leverage, not just features.
  • Production builder: You've built and scaled ML/AI systems in production with meaningful business impact.
  • Ambiguity: You operate effectively in high ambiguity, turning unclear problems into a clear direction.
  • Influence: You've led or influenced large, cross-team technical initiatives.
  • Originality: You introduce new ideas, architectures, or paradigms — not just implement existing ones.
  • Owner-operator: You bring a founder / owner-operator mindset: you take ownership, 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.
  • Vertical conviction: You bring genuine interest in winning a specific industry vertical (real estate) rather than chasing horizontal AI hype.
Must Have
  • Master's or Ph.D. in Computer Science, Machine Learning, or a related field (required).
  • 10+ years of experience building software systems, with significant focus on ML/AI (or equivalent impact).
  • Combined academic and industry track record: Published research and shipped production systems.
  • Deep ML expertise: Traditional Machine Learning, Deep Learning, and Generative AI / LLMs (prompting, fine-tuning, RAG, agents, tool and skills use).
  • LLM post-training: Direct, hands-on experience with LLM post-training — SFT, RLHF, DPO, and/or RL — at non-trivial scale.
  • Full ML lifecycle: Strong understanding of data extraction, model training, evaluation, deployment, and integration into production software.
  • Core stack: Expert in Python, PyTorch, NumPy, AWS, Docker, SQL, embeddings, and RAG.
  • Agent tooling: Experience with LangChain, LangGraph, and LLM observability tools (LangSmith).
  • Production ML at scale: Experience designing and operating production-grade ML systems at scale.
  • Ontology & knowledge graphs: Applied experience with ontology-driven systems, knowledge graphs, or semantic layers used to model business domains for AI systems.
  • AI-native engineering: Proficiency with AI coding tools and workflows (e.g., Copilot, ChatGPT, code generation tools).
Nice to Have
  • Reinforcement Learning depth: Deep RL expertise applied to sequential decision-making under partial observability.
  • Experience designing evaluation and benchmarking systems for AI.
  • Background in distributed systems and real-time architectures.
  • Experience building platforms used by multiple engineering teams.
  • Contributions to industry thought leadership (publications, talks, open source, etc.).
Location
Find out more about our locations by visiting our site. 
All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process.
Compensation & Benefits
The compensation that we reasonably expect to pay for this role is: $264,000 - $330,000 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.

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