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Remote Embedded Audio Engineer Jobs in Virginia (NOW HIRING)

Robotics Engineer

Mclean, VA · On-site +1

$99K - $225K/yr

Remote Work: Hybrid Job Number: R0238340 Location: McLean,VA,US Share job via: Share Robotics ... embedded systems, including driver development and integration of sensors, platforms, and ...

... embedded deployments * networking or hybrid connectivity * identity or security systems ... Experience designing solutions for remote, austere, power-constrained, or intermittently connected ...

Senior Flight Software Engineer

Reston, VA · On-site +1

$127K - $168K/yr

Expertise in real-time operating systems (RTOS) and software architecture for embedded systems ... Our positions are based in Reston, Virginia, with much of our team operating in a hybrid or remote ...

Cybersecurity Engineer - DSOP

Chantilly, VA · Remote

$160K - $200K/yr

None Potential for Remote Work: ORA_ON_SITE Description SAIC is a trusted leader in delivering ... Familiarity with secure by design and shift left security principles embedded across DSOP ...

Showing results 21-40

Remote Embedded Audio Engineer information

Can a remote embedded audio engineer work remotely?

Yes, a remote embedded audio engineer can work remotely, as many companies in this field offer telecommuting options. The role typically involves designing and testing audio hardware and firmware, which can often be done using remote collaboration tools, provided the engineer has access to necessary hardware and software. However, some tasks may require on-site presence for hardware testing or equipment setup, depending on the employer's requirements.

What is a remote embedded audio engineer?

A Remote Embedded Audio Engineer is a professional who designs, develops, and tests audio processing software and hardware that is integrated directly into electronic devices, such as smartphones, automotive systems, or IoT devices, while working remotely. Their responsibilities often include programming embedded systems, optimizing audio algorithms, and troubleshooting audio-related issues. They collaborate with cross-functional teams using digital communication tools and must ensure audio quality, performance, and compatibility within embedded platforms. Working remotely allows them to contribute to projects from anywhere, making use of cloud-based development environments and remote debugging tools.

How does a remote embedded audio engineer typically collaborate with cross-functional teams?

As a Remote Embedded Audio Engineer, you will regularly collaborate with hardware engineers, software developers, and product managers through virtual meetings, project management platforms, and code repositories. Effective communication is essential, as you’ll be responsible for integrating audio solutions into embedded systems and ensuring alignment with overall product goals. Remote team members often rely on clear documentation, regular updates, and collaborative debugging sessions to overcome technical challenges and maintain progress. Building strong virtual working relationships and proactively reaching out for clarifications are key to success in this distributed environment.

What is the difference between Remote Embedded Audio Engineer vs Remote Audio Software Developer?

AspectRemote Embedded Audio EngineerRemote Audio Software Developer
CredentialsBachelor's in Electrical Engineering, Audio Engineering, or related field; experience with embedded systemsBachelor's in Computer Science, Software Engineering, or related; programming skills in C++, Python
Work EnvironmentEmbedded hardware platforms, audio processing units, development labsSoftware development environments, cloud platforms, digital audio workstations
Industry UsageConsumer electronics, automotive, professional audioMusic apps, digital audio platforms, audio processing software
Common Search/ComparisonRemote Embedded Audio Engineer vs Remote Audio Software Developer

The main difference lies in their focus: Remote Embedded Audio Engineers work on hardware-based audio systems, integrating audio processing into embedded devices, while Remote Audio Software Developers focus on creating software applications for audio processing. Both roles require strong technical skills, but their work environments and end products differ significantly.

What are the key skills and qualifications needed to thrive as a remote embedded audio engineer, and why are they important?

To excel as a Remote Embedded Audio Engineer, you need a solid background in embedded systems, digital signal processing, and audio engineering, typically supported by a degree in electrical engineering or a related field. Familiarity with programming languages like C/C++, audio codecs, real-time operating systems (RTOS), and tools such as MATLAB or LabVIEW is essential, along with experience in version control systems like Git. Strong problem-solving skills, attention to detail, and effective remote communication abilities are crucial soft skills in this role. These competencies ensure the reliable design and integration of audio solutions in embedded systems, enabling clear collaboration and high-quality product delivery from a remote setting.

Is there a demand for remote embedded audio engineers?

Remote embedded audio engineers are in growing demand due to the increasing use of embedded systems in consumer electronics, automotive, and IoT devices. Skills in digital signal processing, programming languages like C/C++, and experience with hardware platforms such as ARM or FPGA are highly valued in this field.
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Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Richmond, VA • Remote

$121K - $160K/yr

Full-time

Posted 21 days ago


Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)

AMC Health · Remote (US) · Full-time

The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you'll work across

Real-time voice AI

  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves

  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves

  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

Work authorization (no exceptions)

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply

Please submit both of the following:

  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.