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Av Programmer Remote Jobs (NOW HIRING)

NETGEAR

Sr. Systems Engineer, ProAV - South Central

$136K - $157K/yr

The Sr. Systems Engineer, ProAV serves as a technical expert supporting NETGEAR's ProAV solutions ... Deliver remote and on-site training covering AV-over-IP network design, switch profile selection ...

General Motors

Senior Manager, Embodied AI

Sunnyvale, CA · On-site +1

$296K - $453K/yr

... AV model performance. You will manage AI/ML Engineers, support their career growth, and guide the ... Remote/Hybrid: This role is categorized as fully remote or hybrid. Compensation: The compensation ...

Sonance

Sales Engineer, Professional

San Clemente, CA · On-site +1

San Clemente, CA or Remote Description The US Sales Engineer for Professional Solutions will have a ... This role requires someone who thrives on building strong relationships with AV systems designers.

AVI-SPL

Senior Project Engineer

$101K - $132K/yr

Review project Scope of Work (SOW) to ensure quality and completeness so that remote/regional ... Ability to read, understand and augment AV infrastructure plans and architectural coordination ...

AVI-SPL

Senior Project Engineer

$101K - $132K/yr

Review project Scope of Work (SOW) to ensure quality and completeness so that remote/regional ... Ability to read, understand and augment AV infrastructure plans and architectural coordination ...

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Av Programmer Remote information

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How much do av programmer remote jobs pay per hour?

As of Jul 7, 2026, the average hourly pay for av programmer remote in the United States is $39.54, according to ZipRecruiter salary data. Most workers in this role earn between $25.72 and $51.44 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AV Programmer working remotely, and why are they important?

To thrive as a remote AV Programmer, you need expertise in AV system design, programming languages like Crestron, AMX, or Extron, and a solid understanding of audio/visual signal flow, usually supported by relevant certifications. Familiarity with AV control software, network configuration tools, and platforms like Crestron Toolbox or AMX NetLinx Studio is typically required. Strong problem-solving abilities, self-motivation, and effective remote communication skills help distinguish top performers in this role. These competencies ensure the delivery of high-quality, reliable AV solutions and seamless collaboration with clients and teams from a distance.

What is an AV Programmer and what do they do in a remote role?

An AV Programmer, or Audio Visual Programmer, specializes in designing and coding the software that controls AV systems such as projectors, video conferencing equipment, lighting, and sound systems. In a remote role, AV Programmers use remote access tools to program, troubleshoot, and support AV systems from off-site locations. They collaborate with clients and on-site teams via video calls, remote desktop software, and other communication tools to ensure that AV systems function seamlessly and meet user requirements.

What is the difference between Av Programmer Remote vs AV Technician?

AspectAv Programmer RemoteAV Technician
CredentialsTypically requires certifications like Crestron, Extron, or AMX programming certificationsOften requires technical training or certifications in AV equipment setup and troubleshooting
Work EnvironmentPrimarily remote, focusing on programming and software developmentOn-site, involving equipment installation, setup, and maintenance
Industry UsageCommon in corporate, broadcast, and event production sectors for system programmingUsed across live events, corporate AV setups, and installation services

While Av Programmer Remote focuses on software programming and system integration remotely, AV Technicians are hands-on, working directly with AV hardware on-site. Both roles are essential in the AV industry but differ mainly in work environment and specific skill sets.

What types of projects and collaboration can I expect as a remote AV Programmer?

As a remote AV Programmer, you'll typically work on projects involving the integration and automation of audio-visual systems for clients in sectors like corporate, education, or entertainment. Collaboration is frequent with project managers, AV engineers, and sometimes directly with clients, often through virtual meetings and shared platforms. You'll likely contribute to tasks such as programming control systems (e.g., Crestron, AMX, Extron), troubleshooting remotely, and occasionally providing user training or documentation. Effective communication and proactive problem-solving are key to success in this remote team environment.
More about Av Programmer Remote jobs
What cities are hiring for Av Programmer Remote jobs? Cities with the most Av Programmer Remote job openings:
What are the most commonly searched types of Av Programmer jobs? The most popular types of Av Programmer jobs are:
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Av Programmer Remote jobs near you
Infographic showing various Av Programmer Remote job openings in the United States as of July 2026, with employment types broken down into 82% Full Time, 10% Part Time, 1% Temporary, 6% Contract, and 1% Nights. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $82,234 per year, or $39.5 per hour.

AV Simulation Domain Expert (Sr. Principal) - US (Remote) or Chicago, IL

HERE Technologies

Remote

Other

Posted 4 days ago

Job description

What's the role?
HERE Technologies sits at a unique intersection: we own some of the world's most detailed map and drive data, and we are building the generative AI capabilities to turn that spatial intelligence into controllable, high-quality synthetic driving worlds.
We are looking for a rare hybrid profile - someone who combines deep learning expertise in world foundation models, generative video, and transformers with hands-on AV simulation experience. You understand both how to train and adapt large generative models (think Cosmos, Cosmos-Transfer, diffusion-based video models, latent world models) and how to ground them in map data and scenario semantics so the output is actually useful for training and validating perception and planning stacks.
This is not a pure simulation role, and it is not a pure ML research role. It is the bridge between the two - and that bridge is where HERE's differentiation lives.
What you will do:
World Foundation Models & Generative Scenario Synthesis
  • Drive the technical direction for map-grounded world foundation models: how we condition generative video and world models using map data, drive data, and scenario semantics.

  • Train, fine-tune, and adapt generative models (diffusion, latent video, transformer-based world models) for driving scenario generation, including domain adaptation, controllability, and conditioning on structured inputs (maps, trajectories, agent behaviours, weather, lighting).

  • Evaluate and extend state-of-the-art foundation models such as NVIDIA Cosmos / Cosmos-Transfer and comparable open-source world models, assessing fit for AV training data generation.

  • Own the full ML lifecycle end-to-end: data curation, model training, evaluation, iteration, and the path to production-grade pipelines.

Strategic role
  • Lead proof-of-concept initiatives demonstrating map-grounded synthetic scenario generation with key technology partners.

  • Define measurable success criteria that go beyond visual realism - focusing on ML training data utility, controllability, and sim-to-real transfer.

  • Deliver POC outcomes with clear GO / PIVOT / NO-GO recommendations backed by quantitative evidence.

Simulation, Scenario Generation & Sim-to-Real
  • Bridge generative world models with classical simulation stacks (CARLA, NVIDIA Drive Sim, AlpaSim) where structured, physics-grounded scenarios are needed.

  • Author and programmatically generate OpenSCENARIO / OpenDRIVE definitions that feed both classical simulators and generative pipelines.

  • Drive sim-to-real strategy: measure domain gap, identify failure modes, and define acceptable thresholds for downstream model training.

Quality Frameworks for Synthetic Training Data
  • Define what "good enough" synthetic data means for AV perception and planning: when is photorealism required, when is label consistency sufficient, when does controllability matter most?

  • Establish validation frameworks combining objective metrics (distribution coverage, label accuracy, FID-style measures, downstream task performance) with expert evaluation protocols.

  • Specify sensor fidelity requirements: noise models, lens distortion, lidar return characteristics - and how generative models should or should not reproduce them.

Technical Collaboration
  • Interface with ML research teams on generative model architecture, controllability, and conditioning strategies.

  • Collaborate with perception and planning teams to ensure synthetic data measurably improves real-world model performance.

  • Translate business requirements into technical feasibility assessments for product and executive stakeholders.

Who are you?
This role requires depth in both deep learning and AV simulation. We are not looking for a pure simulation engineer, and we are not looking for a generalist ML researcher without AV grounding.
Must-Have: Deep Learning & Generative Models
  • Proven experience training deep learning models end-to-end, with clear ownership across data, training, evaluation, and iteration.

  • Expertise in generative video, world models, or related generative AI research/engineering.

  • Deep working knowledge of diffusion models, latent video models, and/or transformer-based world models.

  • Experience with high-dimensional temporal or spatio-temporal data (video, multi-sensor fusion, driving data).

  • Strong Python and PyTorch engineering fundamentals; comfortable building research-grade tooling that can scale toward production.

  • Demonstrated ability to take ML models from research into production, navigating real-world constraints, quality, and safety requirements.

Must-Have: AV Simulation & Scenario Domain
  • 5+ years combined experience spanning AV simulation, perception/ML for AVs, or robotics simulation - with meaningful exposure to both simulation platforms and ML model development.

  • Hands-on experience with at least one major simulation platform: CARLA, NVIDIA Drive Sim, or equivalent.

  • Fluency with OpenDRIVE and OpenSCENARIO: can author and generate scenario definitions programmatically and understands map format specifications.

  • Understanding of AV testing workflows: scenario-based validation, ASAM OpenX standards, and awareness of frameworks such as ISO 34502.

  • Understanding of what scenarios stress-test AV perception and planning systems, and why.

Must-Have: Synthetic Data Quality & Sim-to-Real
  • Ability to evaluate synthetic data for ML training utility: distribution diversity, label consistency, edge-case coverage, downstream task performance.

  • Experience with synthetic-to-real transfer, domain adaptation, or closing the sim-to-real gap in a measurable way.

  • Clear point of view on trade-offs between photorealism, label accuracy, controllability, and computational efficiency.

Nice-to-Have
  • Hands-on experience with NVIDIA Cosmos, Cosmos-Transfer, or comparable world foundation models.

  • Reinforcement learning experience, particularly where it measurably improved real-world performance.

  • Experience with end-to-end driving models.

  • Automotive, OEM, or other safety-critical ML deployment experience (ISO 26262, SOTIF awareness).

  • Strong publication record in generative models, world models, or AV ML; or significant contributions to open-source ML tooling.

  • Game engine experience (Unreal, Unity) for rendering and sensor simulation pipelines.

  • Experience with PyTorch Lightning or similar large-scale training infrastructure.

Personal Attributes
  • Bridge-builder: fluent translator between ML researchers, simulation engineers, AV domain experts, and product managers.

  • Hands-on: you validate assumptions by training models and running simulations, not by writing specs.

  • Quality-obsessed: you define objective standards where others see subjective judgments.

  • Pragmatic: you balance "state-of-the-art realism" against "measurably useful for training."

  • Systems thinker: you understand how every choice in data generation propagates into downstream model performance.

Who are we?
As ADAS/AD moves towards model-driven intelligence, industry value is extending from map delivery to model training and validation. HERE can convert its map and drive data into a scalable AI model-creation platform - capturing significant value from training, validation and next generation ADAS/AD performance.
It's the growth of HERE's AI-model creation platform that turns maps and drive data into reusable spatial intelligence - powering scalable training, validation, and next generation ADAS/AD performance.

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