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Flexible Remote Machine Learning Engineer Jobs in Colorado

Cleerly

Sr. Machine Learning Software Engineer

Denver, CO · On-site +1

$126K - $166K/yr

While we are mostly a remote company, travel is required for some team meetings and cross function ... About the Opportunity We are seeking a senior machine learning software engineer to design, build ...

Cleerly, LLC

Sr. Machine Learning Software Engineer

Denver, CO · On-site +1

$126K - $166K/yr

While we are mostly a remote company, travel is required for some team meetings and cross function ... About the Opportunity We are seeking a senior machine learning software engineer to design, build ...

Planet Labs PBC

Senior Geospatial AI/ML Engineer

Denver, CO · Remote

$107K - $147K/yr

The ideal candidate expresses great passion for machine learning, programming, remote sensing, and large-scale scientific computing. This is a full-time, hybrid role which will require you to work ...

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Flexible Remote Machine Learning Engineer information

How does a flexible remote work arrangement impact collaboration and project delivery for Machine Learning Engineers?

In a flexible remote setting, Machine Learning Engineers often rely on digital collaboration tools to communicate with team members and manage projects. This setup allows for asynchronous work, enabling engineers to focus deeply on model development and data analysis without constant interruptions. However, it also means proactively scheduling check-ins and maintaining clear documentation are crucial to ensure alignment across distributed teams. While remote work offers autonomy and work-life balance, successful engineers build strong communication habits to keep projects on track and foster effective collaboration with data scientists, product managers, and software engineers.

What is a Flexible Remote Machine Learning Engineer?

A Flexible Remote Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models while working remotely, often with flexible hours. They use programming, data analysis, and statistical skills to create algorithms that solve real-world problems, collaborating with teams through digital communication tools. This role allows for a better work-life balance and can be performed from anywhere with a reliable internet connection. Flexible remote positions are especially popular in the tech industry, where project-based work and results matter more than strict office hours.

What is the difference between Flexible Remote Machine Learning Engineer vs Data Scientist?

AspectFlexible Remote Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, ML, or related fields; experience with ML frameworksBachelor's or higher in CS, Statistics, or related fields; proficiency in data analysis
Work EnvironmentRemote, collaborative teams, project-basedRemote or on-site, data analysis-focused
Industry UsageTech, finance, healthcare, e-commerceTech, marketing, finance, research
Common Search IntentRoles involving ML model development and deploymentRoles focused on data analysis and insights

The main difference is that a Flexible Remote Machine Learning Engineer primarily develops and deploys machine learning models, while a Data Scientist focuses on analyzing data to generate insights. Both roles often require similar educational backgrounds and can be remote, but their core responsibilities differ in application and focus.

What are the key skills and qualifications needed to thrive as a Flexible Remote Machine Learning Engineer, and why are they important?

To thrive as a Flexible Remote Machine Learning Engineer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and typically a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, cloud platforms (AWS, GCP, or Azure), and experience with data pipelines are essential, and certifications in machine learning or cloud technologies can be advantageous. Excellent communication, self-motivation, and time management skills help you collaborate effectively and stay productive in a remote, flexible work environment. These skills ensure you can independently deliver high-quality ML solutions, maintain clear team communication, and adapt to evolving project requirements.
What are the most commonly searched types of Remote Machine Learning Engineer jobs in Colorado? The most popular types of Remote Machine Learning Engineer jobs in Colorado are:
What are popular job titles related to Flexible Remote Machine Learning Engineer jobs in Colorado? For Flexible Remote Machine Learning Engineer jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Flexible Remote Machine Learning Engineer jobs in Colorado look for? The top searched job categories for Flexible Remote Machine Learning Engineer jobs in Colorado are:
What cities in Colorado are hiring for Flexible Remote Machine Learning Engineer jobs? Cities in Colorado with the most Flexible Remote Machine Learning Engineer job openings:
Flexible Remote Machine Learning Engineer jobs near you

AI Engineer/ML Engineer - Senior Developers - AI Training - Denver, US

Prolific Academic Ltd

Denver, CO • On-site, Remote

$80/hr

Full-time

Posted 19 days ago

Job description

AI & Machine Learning Engineer - AI TrainingAbout Prolific

Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world.

Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.

The role

We're looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models.

Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter.

What you'll bring
  • Education: a BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning.
  • Professional Experience: experience building, deploying, or fine-tuning ML models in a production environment.
  • Deep Learning Mastery: professional-level understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques.
  • LLM Specialization: hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows.
  • Technical Rigor: the ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms.
  • Analytical Critique: high attention to detail in spotting "hallucinations," biased outputs, or logical failures in AI-generated technical content.
What you'll be doing in the role
  • Evaluate LLM Architecture Logic: review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
  • Audit Code & Notebooks: validate ML-specific code (e.g., training loops, data preprocessing scripts, or model evaluations) for efficiency and correctness.
  • Refine RLHF Frameworks: provide the high-quality human feedback necessary to align models with human intent, safety, and helpfulness.
  • Analyze Model Reasoning: critically assess how an AI model navigates complex chain-of-thought (CoT) prompts and identify where the reasoning breaks down.
  • Benchmark Performance: conduct comparative testing between different model outputs based on specific technical taxonomies and performance metrics.
Key Technologies
  • Frameworks: expert proficiency in PyTorch or TensorFlow/Keras.
  • Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers.
  • Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain.
  • Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation.
Why Prolific is a great platform to join as a Participant

Joining our Expert Network will give you the chance to influence the AI models of the future using professional legal expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home.

We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.

We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity.
Links to more information on Prolific

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Privacy Statement

By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.