Machine Learning Engineer 4 (Manager, IC)
for each section heading. Use - for lists. Use
for prose.
We need to preserve all substantive information; do not invent or summarize away detail. So we must keep each bullet exactly as in posting? We can keep the substance but not necessarily verbatim? Probably keep the content but can rephrase slightly? Safer to keep the bullet points as they appear, maybe split lines.
Let's extract Basic Qualifications bullets:
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 4 years of experience programming with Python, Java, Golang, or C++
- At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
- At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
- At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems
Preferred Qualifications bullets:
- Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
- 3+ years of experience optimizing ML algorithms, configurations, and infrastructure
- 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
- 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans.
- 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting)
- 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
- 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation
- Authored/co-authored a paper on a ML technique, model, or proof of concept
Now Responsibilities bullets from "What You’ll Do:" after colon:
List items:
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
- Use programming languages like Python, Scala, or Java
Now About the role paragraph: combine the intro sentences into a paragraph.
Benefits paragraph: "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level."
Pay paragraph: include salary ranges and incentive note. Must preserve all detail. Let's craft:
"The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Chicago, IL: $179,400 - $204,700 for Machine Learning Engineer 4; New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4. Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan."
Now schedule: not present, omit.
Now produce HTML fragment.
We must not include or . Just the fragment.
Let's construct:
About the role
Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
- Use programming languages like Python, Scala, or Java
Requirements
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 4 years of experience programming with Python, Java, Golang, or C++
for prose. We need to preserve all substantive information; do not invent or summarize away detail. So we must keep each bullet exactly as in posting? We can keep the substance but not necessarily verbatim? Probably keep the content but can rephrase slightly? Safer to keep the bullet points as they appear, maybe split lines. Let's extract Basic Qualifications bullets: - Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) - At least 4 years of experience programming with Python, Java, Golang, or C++ - At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) - At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data - At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications bullets: - Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field - 3+ years of experience optimizing ML algorithms, configurations, and infrastructure - 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. - 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. - 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) - 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. - 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation - Authored/co-authored a paper on a ML technique, model, or proof of concept Now Responsibilities bullets from "What You’ll Do:" after colon: List items: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications - Retrain, maintain, and monitor models in production - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale - Construct optimized data pipelines to feed ML models - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI - Use programming languages like Python, Scala, or Java Now About the role paragraph: combine the intro sentences into a paragraph. Benefits paragraph: "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level." Pay paragraph: include salary ranges and incentive note. Must preserve all detail. Let's craft: "The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Chicago, IL: $179,400 - $204,700 for Machine Learning Engineer 4; New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4. Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan." Now schedule: not present, omit. Now produce HTML fragment. We must not include or . Just the fragment. Let's construct:
About the role
Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
- Use programming languages like Python, Scala, or Java
Requirements
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 4 years of experience programming with Python, Java, Golang, or C++