Saviynt
Principal Software Engineer, AI Platform Engineering
El Segundo, CA · Principal
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
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · Los Angeles-Long Beach-Anaheim, 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
Green-card follow-through: 50%
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
- You set the architectural direction for how training data flows, evolves, and is governed across the AI Platform.
- AI Data Lake on GCS: bucket layout, raw → silver → gold tier separation, CMEK encryption, lifecycle rules
- Batch pipelines: Spark on Dataproc for TB-scale feature backfills, Iceberg compaction, and daily S3→GCS incremental sync
Responsibilities
- You define the standards ML engineers and scientists build on, and ensure every training signal is tenant-isolated, PII-free, and traceable from source to model.
- RAG data pipeline: build embedding generation pipelines that chunk, encode, and upsert document embeddings into the vector store; own the data refresh cadence and staleness SLAs for retrieval context
Requirements
- Service APIs: expose data platform services (feature serving, embedding upsert, schema validation) over HTTPS with mTLS and gRPC where low-latency streaming is required
Nice to have
- Differential privacy or k-anonymity for ML training datasets
- Open source contributions: Feast, Great Expectations, Apache Beam, or dbt
- Familiarity with IAM / access governance data: entitlements, provisioning events, access graphs
- Iceberg or Delta Lake at petabyte scale
- Work on a large-scale, Kubernetes-based SaaS platform
- Solve challenging cloud and reliability problems at scale
- Protobuf / Avro compatibility rules, breaking-change migrations in production
- Orchestration at scale:
Compensation
- Competitive compensation, benefits, and growth opportunities
This listing is sourced directly from Saviynt's careers page and normalized into a canonical job model.