Machine Learning Scientist at Tacit
San Francisco, CA
$180,000–$270,000
Jul 22, 2026
San Francisco, CA
Jul 23, 2026
What this job asks for AI summary
A machine learning scientist role at a stealth-stage hardware startup, focused on building and optimizing AI models that process multimodal biosignals — including time series, spatial, and spectral data — from custom sensing hardware. The work spans deep learning research, neural network architecture development, real-time inference, and model evaluation, in close collaboration with hardware engineers and neuroscientists. Suits a PhD-level researcher with a strong deployment track record.
Senior level · San Francisco-Oakland-Berkeley, CA · Full-time
Pay in the description: $180,000–$270,000
“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 570 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (data scientists). range 210–740
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 $173,851 (middle half $133,298–$217,653). 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 (8)
The title is 'Machine Learning Scientist' with no level modifier — advertised seniority is Unspecified. However, the PhD requirement (or equivalent industry experience), expectation of a publishing/deployment track record, and independent ownership of foundational research all point to a Senior-level role.
Degree requirement is set to None because the JD explicitly accepts 'equivalent industry experience' in lieu of a PhD.
PyTorch and TensorFlow are listed as interchangeable examples of deep learning frameworks under Requirements; PyTorch is used as the primary name with TensorFlow in alternatives.
The role sits at the boundary between Data Scientists (15-2051) and Software Developers (15-1252): it involves substantial model building and real-time deployment on custom hardware, which has a strong software engineering flavor. 15-2051 was chosen as primary because the core framing is ML research, algorithm design, and model evaluation rather than general software development.
Preferred qualifications (ASR/neural interfaces, consumer wearables, low-latency/edge inference) are listed under 'Preferred Qualifications' and are marked accordingly.
'Neural network architectures', 'multimodal learning', and 'time series analysis' are concrete technical capabilities explicitly required in the Requirements section; they are retained as named technical skills despite being somewhat broad, because the JD gates on them explicitly.
The role is explicitly onsite in San Francisco (SOMA); remote=false.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): time series analysis, edge inference optimization.
Read the full posting
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