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Remote Audio Signal Processing Machine Learning Jobs in Ashburn, VA

... process yet driven by passion and innovation. Over the following decade, we fine-tuned our craft ... Remote Work: Niyam understands the value of flexibility. We offer remote work. * Career Growth:

Software Engineer, Senior

Herndon, VA · On-site +1

$126K - $166K/yr

Develop and integrate machine learning workflows - including training data preparation, model ... Background in signal processing, image processing, or remote sensing data workflows. * Experience ...

Software Engineer, Senior

Herndon, VA · On-site +1

$126K - $166K/yr

Develop and integrate machine learning workflows -- including training data preparation, model ... Background in signal processing, image processing, or remote sensing data workflows. * Experience ...

AI/ML Engineer

Washington, DC · On-site +1

$130K - $170K/yr

The ideal candidate has strong expertise in applied machine learning, modern LLM technologies, edge ... Experience applying AI to sensor analytics, time-series data, or signal processing. * Experience ...

Showing results 21-40

Remote Audio Signal Processing Machine Learning information

See Ashburn, VA salary details

$30.2K

$86.4K

$175.4K

How much do remote audio signal processing machine learning jobs pay per year?

As of Aug 17, 2026, the average yearly pay for remote audio signal processing machine learning in Ashburn, VA is $86,365.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,100.00 and $115,600.00 per year, depending on experience, location, and employer.

What is the difference between Remote Audio Signal Processing Machine Learning vs Remote Audio Engineering?

AspectRemote Audio Signal Processing Machine LearningRemote Audio Engineering
Required CredentialsKnowledge of machine learning, signal processing, programming (Python, MATLAB)Audio engineering certifications, audio production experience
Work EnvironmentResearch labs, tech companies, remote collaborationRecording studios, broadcast companies, remote or onsite
Industry UsageDeveloping algorithms for audio enhancement, noise reduction, speech recognitionMixing, mastering, live sound, audio content creation

Remote Audio Signal Processing Machine Learning focuses on developing algorithms using machine learning techniques to improve audio quality and analysis. In contrast, Remote Audio Engineering involves practical audio production, mixing, and recording tasks. Both roles require audio knowledge, but the former emphasizes programming and data science, while the latter centers on sound quality and production skills.

What are popular job titles related to Remote Audio Signal Processing Machine Learning jobs in Ashburn, VA?

For Remote Audio Signal Processing Machine Learning jobs in Ashburn, VA, the most frequently searched job titles are:

What job categories do people searching Remote Audio Signal Processing Machine Learning jobs in Ashburn, VA look for?

The top searched job categories for Remote Audio Signal Processing Machine Learning jobs in Ashburn, VA are:

What cities near Ashburn, VA are hiring for Remote Audio Signal Processing Machine Learning jobs?

Cities near Ashburn, VA with the most Remote Audio Signal Processing Machine Learning job openings:

Sr. Distinguished Machine Learning Engineer (Remote-Eligible)

Capital One

Mclean, VA • On-site, Remote

$105K - $145K/yr

Full-time

Re-posted 12 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 147 frontline employees who took The Breakroom Quiz

91st of 171 rated banks


Job description

Sr. Distinguished Machine Learning Engineer (Remote-Eligible)

Overview:

At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.

Team Description:

The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One's consumer products and organizations, by providing well-managed, self-service, experimentation-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real-time customer experiences at scale - turning every channel into a context-aware decisioning surface, from home feeds to marketing and servicing messages. The org's mission is to move Capital One to deliver always-on, cohort-of-one personalization, powered by resilient data foundations, production-grade ML and GenAI systems, and low-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment.

What you'll do in the role:

  • Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services.

  • Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users.

  • Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations.

  • Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability.

  • Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals.

  • Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability.

  • Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems.

  • Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.

  • Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation.

The Ideal Candidate:

  • You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good.

  • Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production.

  • You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven.

  • You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss.

  • You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown.

Capital One is open to hiring a Remote Employee for this opportunity

Basic Qualifications:

  • Bachelor's degree

  • At least 10 years of experience designing and building data-intensive solutions using distributed computing

  • At least 7 years of experience programming in C, C++, Python, or Scala

  • At least 4 years of experience with the full ML development lifecycle using modern technology in a business critical setting

Preferred Qualifications:

  • 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure); Master's or PhD in Computer Science or a relevant technical field.

  • 5+ years of proven expertise in designing, implementing and scaling personalization platform and recommendation systems serving one or more areas of Feed Personalization/Ads Ranking/Targeted Marketing Messaging.

  • 5+ years of strong proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow).

  • 5+ years of experience developing and applying state-of-the-art techniques for optimizing training and inference systems to improve hardware utilization, latency, throughput, and cost.

  • 5+ years of deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment.

  • Passion for staying on top of the latest AI research and AI systems, and judiciously apply novel techniques in production

  • Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers

  • Proven leadership in driving platform strategy, fostering cross-functional collaboration, and influencing technical direction across the company.

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.

Remote (Regardless of Location): $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer


McLean, VA: $314,800 - $359,300 for Sr Distinguished 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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