At INflow Federal, we're not just navigating the frontier of digital transformation; we're reshaping it. Our dedication to merging the prowess of humans and machines to solve complex problems has set us apart in designing and engineering solutions for the Department of Defense (DoD) networks. Here, every challenge is an opportunity to advance, and every solution is a step towards a more secure and connected future. We look forward to welcoming you to the INflow team! ABOUT THIS POSITION: We are looking for an engineer who enjoys disrupting the "norm" to join our dream team designing and executing experiments for few and low shot approaches for overhead imagery. Avenues of research include: self-supervised methods, generative adversarial approaches for domain transfer with synthetic imagery, methods for reducing supervision cost through alternative annotation types and networks. Key Responsibilities: •Design and execute experiments for general detector performance in overhead imagery. Implement and evaluate new network architectures/approaches from literature as necessary to maintain strong baseline performance. •Create custom deep neural network components and new performance metrics as needed. •Occasionally write custom network components, evaluation metrics, etc. in C++ •Maintain containerized execution environments for experiments across compute architectures and domains using Docker and Singularity. •Occasionally work to deploy models created by the team on various partner platforms for evaluation and feedback. •Build and maintain production ready ML pipelines •Build and maintain a production-ready platform to run models in parallel and A/B test •Prepare and preprocess data in collaboration with the data engineering team Required Qualifications <ul><ul><li>Experience in Machine Learning related field, preferably Computer Vision</li><li>In-depth knowledge of image processing, statistical modeling, advanced mathematics, data integration concepts and tools </li><li>Strong organizational, analytical, critical thinking and leadership skills </li><li>Demonstrated leadership on mid-large-scale project impacting strategic partners </li><li>Background in Machine Learning frameworks such as PyTorch, TensorFlow, SparkML or Keras, Scikit Learn </li><li>Proficiency in Python or an equivalent language </li><li>Knowledge of C++ or equivalent object-oriented language</li><li>Experience with at least one cloud provider (AWS, Azure, GCP)</li><li>Experience with CI/CD and containerization tools (e.g. Docker, Singularity, github/GitLab) and best practices </li><li>Experience with Blender</li></ul></ul> Required Education & Experience <ul><ul><li>MS + 2 years </li><li>BS + 5 years </li><li>No Degree + 15 years </li></ul></ul> Preferred Qualifications <ul><ul><li>Masters Degree Preferred</li><li>PhD Preferred</li><li>Research Experience Preferred</li></ul></ul> Clearance Requirement <ul><ul><li>Must have a minimum of a DoD TS/SCI clearance</li></ul></ul> About Us Founded in 2013, INflow Federal has distinguished itself through our exceptional services because of our deep-rooted commitment to our people. The 'INflow difference' lies in our profound appreciation of employee culture, fueling an obsession with the well-being and professional growth of our team. This employee-centric ethos has been pivotal to our enduring success, directly translating into significant benefits for our customers. Our identity is greatly influenced by the contributions of US Military Veterans. INflow's Veteran Outreach Program, dedicated to recruiting transitioning military personnel, has been a resounding success. Veterans now represent over 50% of our workforce, bringing invaluable experience and insight that have been crucial in steering our customers' success. As a dynamic small business at the forefront of innovation, INflow boasts extensive expertise in Network Modernization, Cybersecurity, and Data Modernization in support of Joint Force Mission Operations. Our reach extends to over 20 states, serving prestigious federal government clients including the United States Navy (USN), United States Marine Corps (USMC), and Joint Forces. In our quest to simplify the customer experience, we embrace a collaborative approach, guided by our leadership's transparent communication and unwavering commitment to integrity. Our customers value partnering with INflow for our agility, passion, and integrity. Fueling INflow's continuous evolution is our voracious appetite for researching and integrating AI/ML technologies. We are committed to empowering our employees and customers with these emerging tools, enhancing their ability to effectively integrate these technologies into their workflows. This not only improves their operational efficiency but also deepens their understanding of AI/ML's transformative potential. As we progress, our focus remains steadfast on developing our people so they can lead our customers into the future of mission systems and operations, adeptly navigating the ever-evolving landscape of the Department of Defense with innovative solutions and strategic insights. Citizenship Requirements INflow Federal is a defense contractor. Due to legal requirements, candidates must be US Citizens to be considered for employment. Other Notes - Some travel may be required: Must have valid driver's license and transportation. This is subject to change at the direction of the customer. Other: - Candidate must have the ability to lift up to 50 lbs. Must have willingness to perform duties not listed in the job description as required by INflow and our customer. Equal Opportunity Employer Diversity and Inclusion INflow provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This commitment applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, leaves of absence, compensation, and training. Job applicants and employees are evaluated solely on job-related qualifications and experience.

77 West Wacker Dr (35012), United States of America, Chicago, IllinoisLead Machine Learning Engineer (Remote- Eligible)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. Our team is working on preventing credit fraud using graph databases and huge data sets. Our work focuses on Machine Learning Deliver so most of our time is spent building machine learning applications as opposed to strictly modeling. Our primary work includes gathering data, assisting in training models, testing, deploying and monitoring models as well as other engineering tasks. Most of our code is written in python or scala and we focus on processing data with Spark, but we also use SQL and Snowflake.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. Capital One is open to hiring a Remote Employee for this opportunityBasic Qualifications:Bachelor''s degree At least 6 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 JavaAt least 2 years of experience building, scaling, and optimizing ML systemsPreferred Qualifications:Master''s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code2+ years of experience with data gathering and preparation for ML models2+ years of people leader experience1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud PlatformExperience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization for this position.No agencies please. Capital One is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law. 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 [Click Here to Email Your Resum]. 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 [Click Here to Email Your Resum]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).

Machine Learning Engineer Jobs in Springfield, VA

Machine Learning Engineer
July 22, 2024

Lead Machine Learning Engineer Remote Eligible
July 7, 2024

Machine Learning Engineer
July 6, 2024

Machine Learning Engineer
June 30, 2024

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Machine Learning Engineer Jobs in Springfield, VA

Machine Learning EngineerJuly 22, 2024

Lead Machine Learning Engineer Remote EligibleJuly 7, 2024

Machine Learning EngineerJuly 6, 2024