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Machine Learning Operations Manager Jobs in Austin, TX

Senior Machine Learning Engineer

Austin, TX

$121K - $160K/yr

Help build a first-class machine learning platform from the ground up which manages the entire ... Production operations: Low-level systems debugging, performance measurement, and optimisation on ...

Managers will determine the frequency you need to go into the office to meet priorities. What You'll Do * Build and improve machine learning systems - including LLM-based applications, recommendation ...

Machine Learning Intern

Austin, TX · On-site

$27 - $42/hr

... change management controls for risk and compliance policies Presenting work clearly to technical and non-technical audiences Relevant majors and areas of expertise Data Science Computer Science ...

Collaborate closely with product managers and other engineers to understand business priorities, frame machine learning problems, and architect machine learning solutions for smart bidding, lookalike ...

Machine Learning Scientists III Within the AI & Data organization, the Marketplace Science team ... Partner closely with product, engineering, operations, trust and safety, and other stakeholders to ...

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Help build a first-class machine learning platform from the ground up which manages the entire ... Production operations: Low-level systems debugging, performance measurement, and optimisation on ...

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Help build a first-class machine learning platform from the ground up which manages the entire ... Production operations: Low-level systems debugging, performance measurement, and optimisation on ...

Showing results 21-40

Machine Learning Operations Manager information

What is the difference between Machine Learning Operations Manager vs Data Scientist?

AspectMachine Learning Operations ManagerData Scientist
Primary FocusOverseeing ML deployment, infrastructure, and operational workflowsAnalyzing data, building models, and deriving insights
Required SkillsML deployment, cloud platforms, DevOps, project managementStatistics, programming, data analysis, machine learning algorithms
Work EnvironmentCross-functional teams, engineering, IT infrastructureResearch, data analysis, model development
Common CertificationsCloud certifications, ML Ops certificationsData Science certifications, Python/R expertise

The Machine Learning Operations Manager primarily focuses on deploying and maintaining ML systems in production environments, ensuring operational efficiency. In contrast, Data Scientists concentrate on analyzing data and developing models. Both roles require technical skills, but their responsibilities and work environments differ significantly, making each essential in the AI and data ecosystem.

Senior Machine Learning Engineer

Austin, TX

Roku
Manufacturing • 1 - 5K employees

$121K - $160K/yr

Full-time

Re-posted 28 days ago


Key responsibilities

  • Build and maintain a machine learning platform that manages the entire model lifecycle, including feature engineering, training, versioning, deployment, and monitoring.

  • Apply expertise in data analysis and feature engineering to generate features for multiple use cases and models.

  • Develop and evaluate machine learning models using techniques such as Decision Trees, Logistic Regression, Neural Networks, and Bayesian Analysis for improving system performance and accuracy.


Job description

About the team 

The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and Roku. The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across distributed systems at large scale and with low latency. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation, and Inference Platform that powers the entire landscape, which we continuously evolve over time.

About the role 

We're on a mission to build cutting-edge advertising technology that empowers businesses to run sustainable and highly-profitable campaigns. The Ad Performance team owns server technologies, data, and cloud services aimed at improving the ad experience. We're looking for seasoned engineers with a background in machine learning to aid in this mission. Examples of problems include improving ad relevance, inferring demographics, yield optimization, and many more. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning using both general purpose software and statistical languages.

What you'll be doing 
  • ML infrastructure: Help build a first-class machine learning platform from the ground up which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving/evaluation, and monitoring prediction quality
  • Data analysis and feature engineering: Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models
  • Model training with batch and real-time prediction scenarios: Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving product/system performance, quality, and accuracy
  • Production operations: Low-level systems debugging, performance measurement, and optimisation on large production clusters
  • Collaboration with cross-functional teams: Partner with product managers, data scientists, and other engineers to deliver impactful solutions
  • Staying ahead of the curve: Continuously learn and adapt to emerging technologies and industry trends
We're excited if you have 
  • Bachelors, Masters, or PhD in Computer Science, Statistics, or a related field
  • 5 years of experience in applied machine learning on real use cases 
  • Proficient coding skills and strong software development experience in Spark, Python, or Java
  • Familiarity with real-time evaluation of models with low latency constraints
  • Familiarity with distributed ML frameworks such as Spark-MLlib, TensorFlow, etc.
  • Ability to work with large scale computing frameworks, data analysis systems, and modelling environments i.e. Spark, Hive, NoSQL stores such as Aerospike and ScyllaDB
  • Ad Tech experience is preferred 
  • Proficient use of AI tools and agentic coding practices 
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