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Remote Tesla Machine Learning Engineer Jobs in Virginia

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Remote Tesla Machine Learning Engineer information

What does a Remote Tesla Machine Learning Engineer do?

A Remote Tesla Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models to improve Tesla's products and services. Working from a remote location, they collaborate with teams to analyze large datasets, build predictive models, and optimize algorithms for applications such as autonomous driving, energy management, and manufacturing. They also ensure that machine learning solutions are scalable and meet Tesla's high standards for performance and safety.

What are some common challenges faced by Remote Tesla Machine Learning Engineers, and how can they be overcome?

Remote Tesla Machine Learning Engineers often face challenges such as collaborating across different time zones, ensuring effective communication with cross-functional teams, and maintaining access to high-performance computing resources. To overcome these, engineers typically use collaborative tools for code sharing and project management, participate in regular virtual meetings, and leverage Tesla's robust cloud infrastructure for experimentation and model training. Proactively seeking feedback and staying aligned with team goals are also key practices for success in this remote, fast-paced environment.

What are the key skills and qualifications needed to thrive as a Remote Tesla Machine Learning Engineer, and why are they important?

To thrive as a Remote Tesla Machine Learning Engineer, you need a strong background in computer science, mathematics, and machine learning principles, typically demonstrated through a relevant degree or equivalent experience. Proficiency with Python, TensorFlow or PyTorch, cloud platforms, and version control systems is crucial, and certifications in AI/ML can be advantageous. Exceptional problem-solving, communication, and self-motivation are important soft skills for collaborating remotely and tackling complex projects. These skills enable engineers to design, implement, and scale innovative AI solutions that drive Tesla's technology forward.

What is the difference between Remote Tesla Machine Learning Engineer vs Remote Data Scientist?

AspectRemote Tesla Machine Learning EngineerRemote Data Scientist
Required CredentialsDegree in Computer Science, Engineering, or related field; experience with ML frameworksDegree in Statistics, Mathematics, or related field; strong programming skills
Work EnvironmentCollaborates with engineering teams on autonomous systems and vehicle dataAnalyzes large datasets to extract insights for business or product decisions
Employer & Industry UsagePrimarily in automotive, tech, and autonomous vehicle sectorsAcross tech, finance, healthcare, and various industries

While both roles involve data analysis and machine learning, the Remote Tesla Machine Learning Engineer focuses on developing algorithms for autonomous vehicles, whereas the Remote Data Scientist analyzes data to inform business strategies. The roles share similar credentials but differ in application and industry focus.

What are the most commonly searched types of Tesla Machine Learning Engineer jobs in Virginia? The most popular types of Tesla Machine Learning Engineer jobs in Virginia are:
What cities in Virginia are hiring for Remote Tesla Machine Learning Engineer jobs? Cities in Virginia with the most Remote Tesla Machine Learning Engineer job openings:
Senior Lead Machine Learning Engineer

Senior Lead Machine Learning Engineer

Capital One

Mclean, VA • On-site, Remote

Full-time, Part-time

Posted 26 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 134 frontline employees who took The Breakroom Quiz

72nd 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, or TensorFlow 

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

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

  • 3+ years of people management experience 

  • 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.

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


 

Plano, TX: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer


 

Richmond, VA: $209,000 - $238,500 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 the Capital 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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