Sr. Backend Engineer - Engine Team: API at Deepgram
CA
$150,000–$220,000
Jul 9, 2026
CA
Jul 21, 2026
What this job asks for AI summary
A backend engineering role focused on building and improving speech processing infrastructure, including inference services, distributed compute orchestration, scheduling, and latency and memory optimization. The work spans networking, audio transcoding, and high-performance computing, with some involvement in shaping new features end to end. Best suited to engineers with several years of industry experience and strong Rust or C/C++ skills working on low-level, performance-critical systems.
Mid level · 3+ years · Remote · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 46,300 people nationally plausibly meet what this posting asks for (software developers). range 27,800–69,500
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $107,524–$175,762).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (6)
The title is simply 'backend software engineer' with no level modifier, so advertised seniority is Unspecified. The 3+ years gate and scope of work (improving core services, partnering with Product) are consistent with a Mid-level role.
Rust is the primary required language; C and C++ are listed as explicit alternatives ('Rust (or C, C++)') and are captured as such rather than as separate skills.
PyTorch (listed as 'Torch') and audio processing experience appear under the 'It Would Be Great if You Had' section and are treated as preferred.
ML architecture knowledge (CNNs, RNNs, transformers) is mentioned alongside PyTorch in the preferred section as implementation knowledge rather than a named tool, so it is not emitted as a discrete skill.
No compensation figures are stated in the posting.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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