Senior Machine Learning Engineer

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.

Languages