AHU Technologies Inc
Senior Machine Learning Engineer - United States - Remote
Washington, District of Columbia · Senior
Sponsorship not specified$150k-$300kDetected 109 days ago
Machine LearningMLOps
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odds of building a lasting career here
59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
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.
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About the role
- Interview Process • Initial recruiter screen. • Senior Machine Learning Engineer (Recommendation) technical/product deep dive. • Team/founder conversation.
Responsibilities
- Build recommendation and search across feed, discovery, search, and content continuation.
- Own retrieval and ranking systems, including candidate generation, embeddings, two-tower models, feature engineering, and serving quality.
- Design, launch, and analyze recommendation and search experiments.
Requirements
- Hands-on experience with recommendation, search, ranking, ads ranking, feed ranking, or content discovery systems.
- Experience working on consumer applications, entertainment, social media, gaming, creator platforms, or other engagement-driven products.
- Strong knowledge of two-tower models, embedding retrieval, candidate generation, ranking systems, and online/offline evaluation.
- Experience with consumer applications, entertainment, social, gaming, creator, or engagement-driven products.
- Expertise in two-tower models, embedding retrieval, candidate generation, ranking, and online/offline evaluation.
- Fluency in Chinese (Mandarin) is required.
- 5+ years of production ML experience.
- Experience with search ranking.
- Experience with embedding retrieval.
Compensation
- $150k-$300k
Benefits
- Senior Machine Learning Engineer (Recommendation) technical/product deep dive.
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This listing is sourced directly from AHU Technologies Inc's careers page and normalized into a canonical job model.