Jobs / United States / Abbvie INC
Machine Learning Engineer
Abbvie INC · 🇺🇸 Irvine, CA, US (Remote) · Remote
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 177 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 94 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Abbvie INC paid sponsored hires in similar roles1 certified filing for “SENIOR ENGINEER, SALESFORCE & CONGA” (Software Developers) in CA: $158k–$158k, median $158k. Most were filed at wage level III (100%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Abbvie INC
The US Department of Labor certified 177 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 94 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Abbvie INC →
About the role
Responsibilities • Own small to medium components of machine learning systems from technical designthrough implementation and delivery • Translate technical requirements into high-quality, maintainable code and deliver workstreamsaccording to plan • Build and maintain data pipelines and feature engineering workflows to support machinelearning and AI solutions • Design, train, evaluate, and refine machine learning models with minimal supervision, applyingsound statistical and engineering practices • Implement ML solutions that can be deployed into production environments as microservices,APIs, batch jobs, or streaming components • Support production monitoring efforts by helping define and implement metrics for modelperformance, data drift, anomalies, and retraining triggers • Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, andbusiness stakeholders to deliver project objectives • Understand system design, data models, and technical artifacts well enough to contribute toimplementation decisions and tradeoffs • Follow governance, documentation, coding, and source control standards consistently • Demonstrate flexibility and proactively support teammates with day-to-day responsibilities asneeded • Clearly document and communicate work progress, technical decisions, and outcomes totechnical and non-technical audiences Required Experience & Skills • Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science,Engineering, Operations Research, or other quantitative field • 3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python • Strong programming skills in Python and solid understanding of core computer scienceprinciples • Experience with data manipulation frameworks such as Pandas and PySpark • Experience with machine learning libraries such as scikit-learn, HuggingFace,TensorFlow/Keras, PyTorch, or MLlib • Experience with MLOps practices such as automated model deployment, model performancemonitoring, data drift detection • Working knowledge of SQL and relational data structures • Ability to design, train, and evaluate machine learning models using standard best practicessuch as model selection, validation, bias/variance tradeoffs, and performance assessment • Familiarity with batch and streaming data pipeline concepts such as ETL, ELT, and stream processing • Experience working with cloud environments, preferably AWS • Familiarity with technologies such as APIs, microservices, Docker, and Kubernetes • Strong interpersonal, verbal, and written communication skills • Ability to work effectively in a remote environment using collaboration tools Preferred Experience & Skills • Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science • Experience with managing and architecting solutions on AWS • Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production • Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, • EMR, Sagemaker, DataDog, PagerDuty, Data Cataloging tools, Data Observability tools and Data Governance tools Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: • The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. Thi