Remote · worldwide, UTC+4 acceptedSalary not disclosedfull-timeVerified recentlyHimalayas
We're growing our machine learning team. We're looking for Machine Learning Engineers who own products end to end — from the problem, to production, to the metric that proves it worked.
Responsibilities
Depending on area of focus
Canonical data and entity resolution
Canonical datasets for titles, companies, skills, and industries — the layer every application depends on. Content-addressed IDs, faceted taxonomies, alias graphs accumulated across tens of millions of rows.
Rules-based resolution pipelines with LLM escalation, where the accumulated alias graph is the durable asset and escalation volume should fall over time.
Nightly agent loops that adjudicate ambiguous entities, propose structural changes, and get gated by invariant checks and blast-radius limits before anything commits.
Job ingestion at scale: multi-source feeds, deduplication, freshness, and the indexing economics underneath.
Retrieval, ranking, and matching
Job matching v2: two-tower retrieval with cross-encoder reranking, trained on outcome labels rather than clicks. Hard-negative mining, propensity weighting, impression-time logging.
Mobility embeddings learned from observed career sequences — the similarity a text encoder can't recover, where Claims Adjuster and Underwriting Assistant are substitutable despite sharing no vocabulary.
Pivot feasibility: given where someone is, what moves are realistic, what's missing, and which intermediate roles actually worked for peers.
