Lead Audio ML Engineer at Hark
San Jose, CA
$120,000–$300,000from the description
Jul 15, 2026
San Jose, CA
Jul 21, 2026
What this job asks for AI summary
A lead-level machine learning engineering role focused on audio models that run directly on consumer hardware. The work covers the full pipeline — designing, training, and deploying models for tasks such as wake-word detection, voice activity detection, and speech enhancement — within the tight latency, memory, and power constraints of embedded targets like DSPs and NPUs. Close collaboration with DSP, firmware, and hardware teams is central to the role.
Senior level · 3+ years · National · 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
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
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 job title is 'Lead Audio ML Engineer' but no seniority level word (Senior, Staff, Principal) appears in the title itself, so advertised seniority is Unspecified. The scope and 3+ years requirement support a Senior classification.
SOC classification is a genuine judgment call: the role centers on training and shipping ML models (pointing to 15-2051 Data Scientists), but also involves substantial software engineering — building pipelines, deploying to embedded targets, and integrating with DSP runtimes — making 15-1252 Software Developers a credible alternative.
No work location is specified in the posting. The compensation range is stated as a US base salary range, so the role is treated as US-based. Remote status cannot be confirmed; marked non-remote by default.
The bonus qualifications section lists model compression techniques (quantization, pruning, distillation) and Qualcomm AI stacks as 'familiarity with' items — these are preferred, not required.
'audio deep learning', 'on-device model deployment', 'ML training pipelines', and 'audio signal processing' are named as required capabilities in the Requirements section but are not specific named tools; they are retained here because they represent concrete, domain-specific technical competencies central to the role's hard gates.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): on-device model deployment.
Read the full posting
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