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Automl Jobs (NOW HIRING)

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$153K/yr

Position AutoML, experiment tracking, deployment, monitoring, and CI/CD * Build buyer narrative for DS leaders, ML platform teams, and practitioners * Compete against full-lifecycle platforms and ...

Data Engineer

Malvern, PA · On-site

$112K - $134K/yr

... AutoML, HyperDrive, and Model Registry for experimentation and tracking. • Leverage Azure Functions, Logic Apps, and Event Hubs for event-driven data processing. • Automate data workflows and ...

Vertex AI (training, prediction, pipelines), AutoML, TensorFlow • Experience with at least one data visualization tool, preference: PowerBI. Looker • Strong working knowledge of Cloud platforms ...

Staff iOS Engineer

San Diego, CA · On-site

$184K - $250K/yr

Proficient in building or integrating functional AI models using techniques such as LLM prompting, AutoML modeling, etc * Skilled in evaluating and monitoring the performance of AI technology in ...

Proficient in building or integrating functional AI models using techniques such as LLM prompting, AutoML modeling, etc * Skilled in evaluating and monitoring the performance of AI technology in ...

Staff iOS Engineer

San Diego, CA · On-site

$188K - $255K/yr

Proficient in building or integrating functional AI models using techniques such as LLM prompting, AutoML modeling, etc * Skilled in evaluating and monitoring the performance of AI technology in ...

Staff iOS Engineer

San Diego, CA · On-site

$188K - $255K/yr

Proficient in building or integrating functional AI models using techniques such as LLM prompting, AutoML modeling, etc * Skilled in evaluating and monitoring the performance of AI technology in ...

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Automl information

What are some common challenges faced by professionals working in AutoML roles, and how can they be addressed?

Professionals in AutoML roles often encounter challenges related to automating complex machine learning workflows, ensuring model interpretability, and managing large-scale data pipelines. Balancing automation with customization to meet specific business needs can be tricky, as off-the-shelf solutions may not fit every scenario. Collaborating closely with data scientists, engineers, and domain experts helps in customizing AutoML solutions and overcoming integration issues. Staying updated on the latest tools and frameworks and continuously testing models in production are also essential for success.

What are the key skills and qualifications needed to thrive as an AutoML Engineer, and why are they important?

To thrive as an AutoML Engineer, you need strong proficiency in machine learning, data science, and programming (often Python), typically supported by a degree in computer science, data science, or a related field. Familiarity with AutoML platforms (such as Google AutoML, H2O.ai, or AutoKeras), cloud services, and experience with ML frameworks like TensorFlow or scikit-learn are essential. Analytical thinking, problem-solving abilities, and effective communication help you translate business needs into automated solutions and collaborate with cross-functional teams. These skills are vital for efficiently developing robust, scalable machine learning pipelines that accelerate model deployment and drive business value.

What is the difference between Automl vs Data Scientist?

AspectAutomlData Scientist
Required CredentialsTypically certifications in machine learning, data analysis, or related toolsDegree in data science, statistics, computer science, or related fields
Work EnvironmentFocus on developing and deploying automated machine learning models, often in tech or AI companiesAnalyze data, build models, and generate insights across various industries
Employer & Industry UsageUsed by companies seeking scalable ML solutions, including tech, finance, and healthcareEmployed across industries for data analysis, predictive modeling, and decision support

Automl focuses on automating machine learning processes, making it easier to develop models without extensive coding. Data Scientists, however, perform in-depth data analysis, model building, and interpretation. While Automl tools assist Data Scientists, their roles differ in scope and expertise required.

What is AutoML?

AutoML, or Automated Machine Learning, refers to the process of automating the end-to-end tasks of applying machine learning to real-world problems. This includes steps like data preprocessing, feature selection, algorithm selection, and hyperparameter tuning. AutoML tools are designed to make machine learning more accessible to non-experts and to improve efficiency for experts by reducing the manual effort and expertise needed to build effective models. Popular AutoML platforms include Google Cloud AutoML, H2O AutoML, and Auto-sklearn.
What are the most commonly searched types of Automl jobs? The most popular types of Automl jobs are:
Infographic showing various Automl job openings in the United States as of July 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution.
Product Marketing Manager

$153K/yr

Other

Posted 24 days ago


Job description

As Product Marketing Manager for Analytics & MLOps, you will own the go-to-market for two of Dataiku's most established product surfaces: the visual analytics and data preparation capabilities that displace the competition's legacy tools, and the end-to-end machine learning lifecycle.

Analytics and MLOps are the proven foundation of Dataiku's enterprise platform - the capabilities that hundreds of the world's largest companies use every day to put real models and analytics into production. Your job is to sharpen positioning, drive launches, and grow share in markets where Dataiku already wins.  You'll also work with the PMM and competitive strategy teams to drive competitive battlecards, win/loss analysis, and competitive deal-desk support across Dataiku's major competitors. 

Core Responsibilities

Analytics PMM

  • Position visual data prep, Flow, Stories, dashboards, and BI integrations

  • Drive displacement narrative vs. legacy analytics tools

  • Ship launch assets: pages, one-pagers, demos, sales talk tracks

  • Maintain cadence with the Analytics product team

MLOps PMM

  • Position AutoML, experiment tracking, deployment, monitoring, and CI/CD

  • Build buyer narrative for DS leaders, ML platform teams, and practitioners

  • Compete against full-lifecycle platforms and point solutions

  • Translate productivity outcomes into proof points

Competitive Intelligence

  • Own battlecard development and quarterly refreshes

  • Run monthly win/loss analysis; turn insights into messaging and sales tools

Cross-functional

  • Ladder Analytics & MLOps positioning to Dataiku's category story

  • Regular cadence with Sales, SE, and CS for field feedback

  • Collaborate on integrated launches and analyst engagements

What we're looking for

  • 4-6 years B2B PMM experience in analytics, BI, or ML

  • Bachelor's in marketing, business, CS, or related field

  • Background with Tableau, Power BI, Looker, Alteryx, DataRobot, SageMaker, or similar a plus

  • Strong storytelling, positioning, GTM strategy, sales enablement, and technical fluency in analytics/ML

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