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Databricks Machine Learning Jobs (NOW HIRING)

Vytwo

AI/ML Architect with Databricks , AWS

Prosper, TX · On-site

$59.25 - $77.75/hr

Develop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning. * Conduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large ...

1 point system

Machine Learning Engineer

Fort Mill, SC · Remote

$48/hr

Azure experience is important, especially if they have used Azure Machine Learning, Azure DevOps, Azure Databricks, Azure Functions, or related cloud services. Key Screening Questions Machine ...

Socket.dev

Machine Learning Engineer

Oakbrook Terrace, IL · On-site

$125 - $150/hr

We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional ... Optimize Databricks jobs for performance, scalability, and cost efficiency * Write and maintain ...

Venturegloballng

Engineer, Machine Learning

Arlington, VA · On-site

$157K - $185K/yr

The main data engineering work will be done in Databricks and PySpark. The ideal candidate will ... Integrate machine learning models into production environments, ensuring reliability and ...

Machina Labs

$160K - $190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of ... Leverage Databricks and Apache Spark for large‑scale data processing and model development.

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How much do databricks machine learning jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for databricks machine learning in the United States is $22.82, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $25.48 per hour, depending on experience, location, and employer.

What is Databricks Machine Learning?

Databricks Machine Learning is a cloud-based platform that provides a collaborative environment for building, training, and deploying machine learning models at scale. It integrates with Apache Spark and offers tools for managing the entire machine learning lifecycle, from data preparation to model deployment. The platform supports popular ML frameworks like TensorFlow, PyTorch, and scikit-learn, and includes features such as automated machine learning (AutoML), experiment tracking, and model management. Databricks Machine Learning is designed to help teams accelerate the development and operationalization of machine learning solutions.

What are the key skills and qualifications needed to thrive as a Databricks Machine Learning engineer?

To thrive as a Databricks Machine Learning Engineer, you need a solid background in machine learning, data science, and programming (especially Python or Scala), typically supported by a relevant degree and experience in big data environments. Proficiency with Databricks, Apache Spark, MLflow, and cloud platforms like AWS or Azure is essential, and certifications such as Databricks Certified Machine Learning Professional are highly valued. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and translate business requirements into technical solutions. These skills ensure you can build, deploy, and manage scalable ML models that drive valuable business insights and outcomes.

What are some common challenges faced by professionals working with Databricks Machine Learning, and how can they be addressed?

Professionals in Databricks Machine Learning often encounter challenges such as managing large-scale data efficiently, ensuring data quality, and optimizing distributed machine learning workflows. Collaboration with data engineers and DevOps teams is essential to streamline data pipelines and maintain model reproducibility. Leveraging Databricks' built-in version control, automated ML features, and scalable cluster management can help mitigate these challenges. Staying up-to-date with platform updates and best practices also contributes to smoother project execution.

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

AspectDatabricks Machine LearningData Scientist
CredentialsExperience with cloud platforms, data engineering, ML frameworksDegree in CS, statistics, or related fields; often with certifications
Work EnvironmentCollaborates with data engineers, data scientists on cloud-based platformsAnalyzes data, builds models, often in research or business settings
Tools & SkillsDatabricks platform, Spark, MLflow, Python, SQLPython, R, SQL, statistical analysis, visualization tools

While Databricks Machine Learning focuses on deploying scalable ML models using the Databricks platform, Data Scientists primarily analyze data and develop models, often using various tools and environments. Both roles collaborate closely but differ in technical focus and responsibilities.

Are Databricks good for machine learning?

Databricks Machine Learning is a popular platform for developing and deploying machine learning models, offering integrated tools for data processing, model training, and collaboration. It supports scalable cloud-based environments and integrates with frameworks like TensorFlow and PyTorch, making it suitable for data scientists and ML engineers. Its collaborative workspace and automation features help streamline the machine learning lifecycle.

Does Databricks Machine Learning hire remote employees?

Databricks Machine Learning roles can be remote, depending on the position and company policies. Many companies in the tech industry, including those offering machine learning roles, support remote work arrangements, especially for roles involving cloud platforms and data science tools. Candidates should review specific job postings for location requirements and remote work options.

What are popular job titles related to Databricks Machine Learning jobs?

For Databricks Machine Learning jobs, the most frequently searched job titles are:

Infographic showing various Databricks Machine Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $47,468 per year, or $22.8 per hour.

AI/ML Architect with Databricks , AWS

Prosper, TX • On-site

Vytwo
See more jobs at Vytwoexternal

$59.25 - $77.75/hr

Other

Re-posted 8 days ago

Job description

Job Title: AI/ML Architect with Databricks, AWS

Location: Los Angeles, CA (Hybrid)

Hire type: FTE / CTH

Role Overview

We are seeking an experienced AI/ML Architect with deep hands‑on expertise in Databricks on AWS to lead the design and implementation of scalable, high‑performance data and machine learning platforms. The ideal candidate combines architectural thinking with strong engineering execution, demonstrating the ability to build modern lakehouse systems, optimize large‑scale pipelines, and drive analytical and ML capabilities across the organization.

This role requires working with large, multi‑terabyte datasets, advanced analytics, and end‑to‑end ML lifecycle management using Databricks, Python, PySpark, and AWS‑native services.

Must Demonstrate (Critical Competencies)
  • Designing Databricks‑based lakehouse architectures on AWS (Delta Lake + S3 + Unity Catalog).
  • Clear separation of compute vs. serving layers in distributed architectures.
  • Low‑latency API strategy where Spark is insufficient (e.g., leveraging optimized services or caching).
  • Caching strategies to accelerate reads and reduce compute cost.
  • Data partitioning, file size tuning, and optimization strategies for large‑scale pipelines.
  • Experience handling multi‑terabyte structured time‑series workloads.
  • Ability to distill architectural significance from ambiguous business requirements.
  • Strong curiosity, questioning, and requirement‑probing mindset.
  • Player‑coach approach: hands‑on technical depth + ability to guide design.
Key Responsibilities AI/ML & Advanced Analytics
  • Develop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning.
  • Conduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large datasets.
  • Deploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.
  • Build analytics solutions such as forecasting, anomaly detection, segmentation, or recommendation systems.
  • Design ML architectures aligned with Databricks Lakehouse on AWS.
Data Engineering & Lakehouse Architecture
  • Architect and build scalable ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.
  • Implement Delta Lake best practices, including OPTIMIZE, ZORDER, partitioning, and schema evolution.
  • Design lakehouse layers (Bronze/Silver/Gold) with strong separation of compute and serving layers.
  • Optimize cluster performance and jobs using Spark tuning, caching, and shuffle minimization.
  • Work with multi‑terabyte, time‑series, high‑velocity data in a distributed environment.
  • Ensure robust data availability for downstream ML and analytics workloads.
AWS Cloud Integration
  • Architect end‑to‑end data and ML solutions using AWS services, including:
  • S3 for storage
  • IAM for identity & access
  • Glue Catalog for metadata management
  • Networking for secure, high‑throughput data movement
  • Integrate Databricks with AWS‑native compute, API layers, and low‑latency endpoints.
Business Collaboration & Leadership
  • Translate business problems into scalable analytical or ML architectures.
  • Communicate complex statistical and architectural concepts to non‑technical stakeholders.
  • Collaborate with product, engineering, and business leaders to drive data‑informed initiatives.
  • Provide design leadership while remaining hands‑on in execution.
Skills & Qualifications Required
  • Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.
  • 10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.
  • Deep expertise in Databricks on AWS, including:
  • PySpark / Spark SQL
  • Databricks Notebooks
  • Delta Lake
  • Unity Catalog
  • MLflow
  • Databricks Jobs & Workflows
  • Strong programming ability in Python (pandas, numpy, scikit‑learn).
  • Demonstrated experience with large‑scale, multi‑terabyte data processing.
  • Strong understanding of ML algorithms, distributed systems, and data optimization.
Preferred
  • Experience with MLOps and production deployment pipelines.
  • Strong grasp of AWS‑native data and compute services.
  • Understanding of CI/CD using GitHub Actions, GitLab CI, or similar.
  • Familiarity with deep learning frameworks (TensorFlow, PyTorch).
Key Competencies
  • Strong analytical and problem‑solving skills.
  • Ability to work in fast‑paced, highly collaborative environments.
  • Excellent communication and presentation abilities.
  • Self‑driven with exceptional attention to architectural detail.

Flexible work from home options available.

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