Forward Deployed Engineer - Python developer, exp. in PyTorch at Talent Search PRO
Austin, TX
$150,000–$250,000from the description
Jun 29, 2026
Austin, TX
Jul 21, 2026
What this job asks for AI summary
A customer-facing machine learning engineering role centered on building data pipelines, custom algorithms, and models across computer vision, audio, text, and metadata domains. The work spans translating loosely defined client requirements into production-ready systems, covering everything from research prototyping to inference optimization and quality assurance. Best suited to a Python-proficient engineer with startup experience and strong instincts around dataset quality and evaluation.
Mid level · 1+ years · National
“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
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $120,230 (middle half $85,660–$158,880). 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)
Location is not specified anywhere in the posting; CBSA and state fields are left blank. The role may be remote or in-person at an unspecified startup.
The experience requirement of '1 to 3 years' is unusually low for the $150K–$250K compensation range and the breadth of responsibilities described (customer-facing, production ML, data pipelines). The seniority is assessed as Mid based on the stated years, though the scope and pay skew toward Senior.
The posting is genuinely ambiguous between Data Scientist (ML modeling, algorithms, evaluation) and Software Developer (production pipelines, inference optimization, production systems). Data Scientist is chosen as primary because the core work centers on custom algorithms, model workflows, dataset quality, and evaluation loops; Software Developer is listed as the runner-up.
PyTorch is listed under Nice-to-Have with 'or similar ML frameworks', so alternatives include TensorFlow and JAX.
Computer vision, audio processing, and multimodal data experience are listed under Nice-to-Have, as is large-scale data pipeline experience at scale — despite pipelines also appearing in the Must-Have section, the specific domain (video/audio/multimodal) is explicitly optional.
Open source contribution is listed under Nice-to-Have and is not a hard gate.
Read the full posting
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