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# Backend Software Engineer – Engine Team (Voice Agent)

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/deepgram.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 150k–220k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished29 Oct 2026Apply before28 Nov 2026Listing views36Application actions2Application toolkit

## Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

AI Summary

## The role, at a glance.

Deepgram is hiring a backend software engineer to lead design and implementation for its Voice Agent product. The role focuses on secure, scalable, low-latency services for speech processing, model orchestration, networking, and observability. The engineer will build integrations with telephony, RAG, and AI-model systems while debugging concurrent production workloads and rapidly adapting services to customer needs. Candidates need at least three years of industry experience, Rust or C/C++ expertise, Python competence, Git, and strong UNIX systems knowledge. This is a high-change AI-first environment that values experimentation, ownership, and close collaboration with product teams.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThe position involves production-grade, low-latency distributed systems, concurrent workloads, AI-model orchestration, and speech-processing services. It also requires end-to-end technical ownership and rapid decision-making in a fast-evolving product environment.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive$185,000US market range (per year)$150k–$220k0$242.00k

AI insightThe employer discloses a yearly base compensation range of $150,000–$220,000 USD, with a midpoint of $185,000. This is a credible range for a US-based backend engineer working on high-performance AI and voice-platform infrastructure.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Rust](https://jobicy.com/jobs?search_keywords=Rust.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[C++](https://jobicy.com/jobs?search_keywords=C%2B%2B.md)[Backend development](https://jobicy.com/jobs?search_keywords=Backend%20development.md)[Distributed systems](https://jobicy.com/jobs?search_keywords=Distributed%20systems.md)[Low-latency systems](https://jobicy.com/jobs?search_keywords=Low-latency%20systems.md)[Speech processing](https://jobicy.com/jobs?search_keywords=Speech%20processing.md)[AI model orchestration](https://jobicy.com/jobs?search_keywords=AI%20model%20orchestration.md)[Networking](https://jobicy.com/jobs?search_keywords=Networking.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[Concurrent programming](https://jobicy.com/jobs?search_keywords=Concurrent%20programming.md)[UNIX systems](https://jobicy.com/jobs?search_keywords=UNIX%20systems.md)[Git](https://jobicy.com/jobs?search_keywords=Git.md)[RAG integrations](https://jobicy.com/jobs?search_keywords=RAG%20integrations.md)[Telephony integrations](https://jobicy.com/jobs?search_keywords=Telephony%20integrations.md)

Sample interview questionsHow would you design a low-latency backend service for real-time voice-agent interactions?I would define end-to-end latency budgets, use asynchronous I/O and bounded concurrency, minimize serialization and network hops, and isolate critical inference paths from noncritical work. I would add tracing, latency percentiles, error metrics, and load tests to validate performance under realistic audio and traffic conditions.

Describe an approach to debugging an intermittent issue in a highly concurrent service.

I would first make the issue observable by correlating logs, distributed traces, metrics, and request identifiers. Then I would narrow the failure conditions, inspect scheduling and resource contention, reproduce it with targeted load or fault-injection tests, and verify the fix through regression tests and production monitoring.

What considerations are important when integrating several AI models into a production voice application?

I would define clear interfaces and versioning, manage timeouts and fallback behavior, evaluate model latency and quality trade-offs, and protect customer data with appropriate access controls. I would also instrument each stage so the team can identify bottlenecks, quality regressions, and model-specific failures.

How do you decide between a minimally invasive change and a larger architectural redesign?

I assess customer impact, operational risk, expected lifetime of the solution, and whether the existing architecture can safely support the requirement. For urgent needs, I may choose a contained, well-tested change while documenting debt and defining concrete triggers for a later redesign.

How have you used AI tools to improve your engineering workflow?

I use AI tools to accelerate investigation, summarize unfamiliar code paths, draft test cases, and explore implementation alternatives, while validating all outputs through code review, testing, and system-level reasoning. The goal is faster iteration without compromising correctness, security, or maintainability.

Opportunity details

## About this role.

### Company Overview

Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.

### Company Operating Rhythm

At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.

Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.

Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.

Opportunity

Deepgram is looking for a backend software engineer to lead the design and implementation of Deepgram’s Voice Agent product. You will design and implement secure, robust, and scalable services for speech processing; build integrations supporting telephony providers, RAG systems, and diverse deployment scenarios; engineer for testability and observability within a complex chain of AI models; and more. Your skill at building highly reusable code that overcomes technical challenges is paired with an intuition for delightful user experiences. You will be a critical voice in Deepgram’s Product and Engineering teams, driving high impact products from start to finish.

What You’ll Do

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Improve Deepgram’s core inference services including areas in networking, speech processing, model orchestration, and observability

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Develop integrations with cutting edge in-house, third-party, and open-source AI models for perception and managing conversational dynamics

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Debug complex system issues that include networking, scheduling, and highly concurrent workloads

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Rapidly customize backend services to support our customer needs

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Partner with Product to design and implement new services, features, and/or products end to end

You’ll Love This Role If You

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Thrive in a fast-paced, impact-driven environment where learning new skills on-the-fly is not only encouraged but a regular necessity

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Enjoy balancing decisions about product and feature maturity to decide when to make minimally invasive changes versus when to incorporate detailed design work

It’s Important To Us That You Have

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3+ years of experience in an industry role

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Programming experience in Rust (or C, C++), with competence in Python

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Excellent communication and organizational skills, both written and verbal.

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A high level of experience and understanding of version control; preferably git.

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Comprehensive experience with UNIX-style systems.

It Would Be Great If You Had

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Experience with low-latency, multi-model orchestration for AI-enabled applications

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Experience with audio processing

Notice: We’re aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @[deepgram.com](http://deepgram.com) email address. If you’ve received a message claiming to be Deepgram, please forward it to careers@deepgram.com.

Show more

[Apply now >](https://jobicy.com/jobs/157859-backend-software-engineer-engine-team-voice-agent.md)

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