Kiddom
Senior Machine Learning Engineer
San Francisco · Senior · Full-time
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
$8,149 more — $193,149 — moves this role to Level III and 3 lottery entries. That figure is inside the range the employer already advertised.
Based on the DOL prevailing wage for this occupation and worksite, a base salary of $193,149 would place this position at wage Level III. That figure is within the posted range, and I'd like to target it. This role classifies under "Software Developers" for prevailing-wage purposes.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- Through unparalleled curriculum management functionality, Kiddom empowers schools and districts to take ownership of their curriculum - resulting in learning experiences tailored to meet the unique needs and goals of local communities.
- Salary range is dependent on geographic location, prior experience, seniority, and demonstrated role related ability during the interview process.
- Average use 4 weeks off per year. * 10 paid sick days per year (pro rated depending on start date) * Paid holidays * Paid bereavement leave * Paid family leave after birth/adoption.
Responsibilities
- Design intelligent discovery pipelines that combine semantic retrieval, curriculum alignment, and real-time personalization.
- Build agentic assistants that help teachers plan lessons, adapt instruction, and reduce repetitive tasks.
- Proven track record designing, evaluating, and deploying ML/AI systems in production environments that drive measurable business impact, ideally in recommendation, personalization, search, or workflow optimization.
Requirements
- Strong analytical skills and ability to break down complex problems into measurable hypotheses and experiments.
- Experience implementing and fine-tuning large language models (LLMs), including prompt engineering, embeddings, and efficient inference optimization.
- Familiarity with foundation model adaptation techniques such as PEFT, LoRA, or RLHF.
Compensation
- Salary range is dependent on geographic location, prior experience, seniority, and demonstrated role related ability during the interview process.
Benefits
- Architect and scale machine learning systems for search, personalization, and recommendations that power Kiddom's teacher helper and insight engine.
- Develop evaluation-first development workflows to measure how models improve teaching efficiency, lesson planning, and student learning outcomes.
- Fine-tune machine learning models with feedback signals from teachers and students to align outputs with instructional goals and classroom needs.
- Collaborate closely with product managers, designers, and curriculum experts to translate high-level educational goals into scalable ML-powered systems.
- Have 5+ years of industry experience applying machine learning to solve real-world problems with large, complex datasets, with 1-2 years in a technical leadership role.
- medical (various PPO/HMO/HSA plans), dental, vision, disability and life insurance * One Medical membership (in participating locations) * Flexible vacation time policy (subject to internal approval).
Visa & Work Authorization
- nd applicants without regard to race, religion, color, gender, sexual orientation, transgender status, national origin, citizenship status, uniform service member status, pregnancy, age, genetic information, disability,
This listing is sourced directly from Kiddom's careers page and normalized into a canonical job model.