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Sr. Backend Engineer – Engine Team: API

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Remote from
USA
Salary
USD 150k–220k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
30 Nov 2026
Listing views
39
Application actions
4
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AI Summary

The role, at a glance.

Deepgram is hiring a senior backend engineer to design and evolve scalable, secure services that power real-time speech AI APIs. The role focuses on inference services, distributed compute orchestration, networking, scheduling, audio transcoding, and low-latency performance optimization. The engineer will troubleshoot complex systems issues, rapidly adapt backend capabilities for customer needs, and partner with Product on end-to-end service development. Candidates need at least three years of industry experience, strong Rust or C/C++ and Python skills, UNIX proficiency, and effective written and verbal communication. The environment is fast-moving and expects active, practical use of AI tools in daily work.

Role DNA

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

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a high-complexity systems engineering role involving real-time inference, distributed compute, low-level networking, and strict latency and memory constraints. Success requires independent technical judgment while working effectively across engineering and product functions.

Salary analysis

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

Estimated job median (per year)Market rate
$185,000
US market range (per year)$160k–$230k
AI insightThe employer discloses an annual base compensation range of $150,000–$220,000 USD, with a midpoint of $185,000. Estimated US market compensation for a senior backend engineer specializing in high-performance distributed systems is approximately $160,000–$230,000 USD per year; actual pay may vary by experience, location, and technical depth.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you investigate elevated end-to-end latency in a real-time speech inference API?

I would first instrument the request path and separate time spent in ingress networking, queueing, scheduling, model inference, audio processing, serialization, and egress. I would compare affected requests by region, model, payload characteristics, and infrastructure version, then use traces and system metrics to isolate the dominant contributor. After validating a hypothesis under representative load, I would implement a measured change and monitor latency percentiles, error rates, throughput, and resource utilization.

Describe how you would design a scalable backend service that schedules inference work across heterogeneous compute resources.

I would define a clear work-item contract containing model requirements, priority, resource estimates, deadlines, and affinity constraints. The scheduler would use health-aware worker discovery, bounded queues, backpressure, admission control, and policies that balance latency-sensitive traffic against throughput-oriented work. I would build observability around queue depth, dispatch delay, GPU or CPU utilization, retries, and tail latency, then load-test failure and burst scenarios before rollout.

What practices do you use to make Rust services robust in production?

I favor explicit ownership boundaries, typed error handling, careful concurrency design, and defensive validation at API boundaries. I use profiling and benchmarks to validate performance assumptions, structured logging and metrics for operational visibility, and thorough unit, integration, and load tests for critical paths. For risky changes, I use feature flags, gradual rollout, and documented rollback procedures.

How would you approach rapidly customizing a core backend service for a customer without creating long-term maintenance debt?

I would clarify the customer's underlying use case, expected scale, success criteria, and timeline before selecting the smallest safe solution. I would prefer configuration, stable extension points, or a generalized capability over a one-off fork when the need is likely reusable. I would document trade-offs, add tests and observability, set an owner and sunset criteria for temporary work, and share the plan with Product and Engineering stakeholders.

How do you incorporate AI tools into engineering work while maintaining quality and security?

I use AI tools to accelerate exploration, documentation, test generation, and debugging hypotheses, but I treat their output as untrusted until reviewed and verified. I avoid exposing sensitive code or customer data to unapproved tools, validate generated changes with tests and profiling, and ensure architecture decisions remain grounded in system requirements. This approach improves speed while preserving code quality, confidentiality, and engineering accountability.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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 products. You will design and implement secure, robust, and scalable services for speech processing; efficient, distributed compute orchestration; optimized scheduling, 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

  • Improve Deepgram’s core inference services including areas in networking, speech processing, audio transcoding, and latency and memory optimization

  • Develop processes for measuring, building, and optimizing services to maximize system performance

  • Debug complex system issues that include networking, scheduling, and high performance computing interactions

  • Rapidly customize backend services to support our customer needs

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

You’ll Love This Role If You

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

  • 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

  • 3+ years of experience in an industry role

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

  • Excellent communication and organizational skills, both written and verbal.

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

  • Comprehensive experience with UNIX-style systems.

It Would Be Great if You Had

  • Experience with modern machine learning, such as experience with a framework like Torch or implementation knowledge of architectures like CNNs, RNNS, and transformers

  • Experience with audio processing

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

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