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Flexible Remote Machine Learning Engineer Jobs in California

Principal Machine Learning Engineer

San Francisco, CA · On-site +1

$159.10K - $213.20K/yr

Collaborating with AI teams to integrate advanced machine learning models into game development ... Flexible work environment with options for remote work. * Competitive salary and benefits, with ...

Senior Machine Learning Engineer

San Francisco, CA · On-site +1

$123.10K - $169.10K/yr

... flexible time-off policies, and mental health and wellness resources to support your overall well ... Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We ...

Location and travel We have a lovely office in Oakland, CA, but we also have remote employees ... Flexible work environment - work from our office in Oakland or remotely as long as you can travel ...

Senior Machine Learning Engineer

San Jose, CA · On-site +1

$161.90K - $194.20K/yr

Whether in one of our offices in San Jose, CA, Draper, UT, or in a remote-eligible role, BILLders ... Employee Assistance Program (EAP) * 11+ Observed holidays and wellness days and flexible time off

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Showing results 1-20

Flexible Remote Machine Learning Engineer information

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.

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 most commonly searched types of Remote Machine Learning Engineer jobs in California? The most popular types of Remote Machine Learning Engineer jobs in California are:
What are popular job titles related to Flexible Remote Machine Learning Engineer jobs in California? For Flexible Remote Machine Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Flexible Remote Machine Learning Engineer jobs in California look for? The top searched job categories for Flexible Remote Machine Learning Engineer jobs in California are:
What cities in California are hiring for Flexible Remote Machine Learning Engineer jobs? Cities in California with the most Flexible Remote Machine Learning Engineer job openings:
Senior Lead Machine Learning Engineer

Senior Lead Machine Learning Engineer

Capital One

San Jose, CA • On-site, Remote

Full-time

Posted 17 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 134 frontline employees who took The Breakroom Quiz

74th of 141 rated banks


Job description

Senior Lead Machine Learning Engineer

As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

What you'll do in the role:

The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:

  • 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.

Basic Qualifications:

  • Bachelor's Degree

  • At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)

  • At least 4 years of experience programming with Python, Scala, or Java

  • At least 3 years of experience building, scaling, and optimizing ML systems

  • At least 2 years of experience leading teams developing ML solutions

Preferred Qualifications:

  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field

  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform

  • 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, Kubeflow or TensorFlow

  • 3+ years of experience developing performant, resilient, and maintainable code

  • 3+ years of experience with data gathering and preparation for ML models

  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

  • 3+ years of experience building production-ready data pipelines that feed ML models

  • Ability to communicate complex technical concepts clearly to a variety of audiences

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

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.

Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer


McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer


New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer


San Francisco, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer


San Jose, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer







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.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).


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