Applied ML Engineer at Macroscope
San Francisco, CA
$170,000–$280,000from the description
Aug 3, 2026
San Francisco, CA
Sep 24, 2026
What this job asks for AI summary
An early-stage AI startup is hiring a Senior Applied ML Engineer to own large portions of the model development lifecycle — building evaluation datasets, designing and running experiments, and training or fine-tuning LLMs (including reinforcement learning techniques). The role sits at the intersection of ML research and production engineering, requiring close collaboration with founders and backend engineers to integrate improved models into a live product that analyzes software codebases.
Senior level · 3+ years · Remote · Full-time
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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 Aug 4, 2026. It is a model, not a headcount.
Why we read it this way (6)
No work location or metro is specified in the posting; the role appears to be remote or location-flexible.
The SOC classification is a close call: the role blends ML research/modeling (15-2051 Data Scientists) with substantial production software engineering (15-1252 Software Developers). It was classified as 15-2051 because model quality, evaluation, and training are described as the primary focus.
Several skills in the Qualifications section are framed as activities or methodologies rather than named tools (e.g., 'dataset creation', 'ML pipelines'). These were retained because they represent concrete, recurring work products central to the role, but no specific framework names were given.
Reinforcement learning techniques (RLHF, RLAIF, GRPO, PPO, DPO) are listed together as a single requirement with interchangeable options; any practical experience is noted as valuable per the posting.
Golang, GCP, Temporal, distributed training, synthetic data generation, and evaluation frameworks are all listed under the Bonus section and are marked as preferred accordingly.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): ML model fine-tuning, Dataset creation and curation.
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