Jobs / United States / Humana INC
Senior Decision Intelligence Engineer
Humana INC · 🇺🇸 Remote Nationwide · 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 260 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 130 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 Humana INC paid sponsored hires in similar roles4 certified filings for “Senior Business Intelligence Engineer” (Data Scientists) in KY: $105k–$141k, median $105k. Most were filed at wage level III (50%) — 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 — Humana INC
The US Department of Labor certified 260 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 130 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 Humana INC →
About the role
Become a part of our caring community The Senior Machine Learning Engineer, Decision Intelligence is a hands-on individual contributor responsible for building, deploying, and operating ML and decisioning pipelines for the NBA Decision Intelligence Platform. This role focuses on production pipeline development, MLOps, feature engineering, scoring workflows, monitoring, and optimization-aware decisioning. You will help ensure the platform selects the right action for the right member while respecting clinical eligibility, suppression rules, channel constraints, program goals, and operational capacity. You will work closely with ML engineers, data engineers, platform engineers, product owners, and decision engine teams to deliver reliable, scalable, and auditable production systems. Additional Job Description Required Qualifications • Bachelors in computer science or relevant field • 5+ years (post undergraduate level) of software engineering or quantitative research experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, optimization engines, or simulation frameworks serving millions of users. • 2+ years (post graduate level) of software engineering or quantitative research experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, optimization engines, or simulation frameworks serving millions of users. • 2+ years of hands-on experience implementing reinforcement learning, operations research methods, or simulation-driven decision systems in production. Relevant backgrounds include policy gradient and value-based RL (PPO, A3C, DQN, CQL), stochastic dynamic programming, discrete-event simulation, or large-scale combinatorial or constrained optimization. • Deep familiarity with Markov Decision Processes, Bellman-equation-based value estimation, reward or objective shaping, exploration-exploitation tradeoffs, and constraint formulation in real-world decision systems. • Demonstrated ability to diagnose failure modes in learned or optimized policies: instability, poor credit assignment across long horizons, and distributional shift across large populations. • Proficiency in Python 3.x; experience with PyTorch or TensorFlow for policy network or learned model implementation. • Experience with Ray RLlib or equivalent distributed computation frameworks for large-scale training or optimization. • Experience with Databricks, PySpark, and Delta Lake for large-scale ML or data pipelines processing tens of millions of records. • Experience with MLflow for experiment tracking, model registry, and artifact management. • Experience with shipping systems that operate reliably under production load, not just research or prototype work. Preferred Qualifications • Experience with multi-agent RL frameworks (PettingZoo or equivalent) or multi-agent simulation and coordination methods. • Familiarity with operations research methods applicable to constrained sequential decisioning: linear programming, mixed-integer programming, Lagrangian relaxation, or constraint programming. • Experience operating decision or optimization systems in regulated domains (healthcare, finance, or insurance) where member safety, auditability, and explainability are requirements. • Experience building simulation environments using Gymnasium, SimPy, AnyLogic, or equivalent frameworks for policy evaluation and backtesting. • Familiarity with event-driven feedback loops and how disposition signals feed retraining or re-optimization pipelines. • OpenTelemetry instrumentation experience for ML or optimization pipeline observability. Use your skills to make an impact Additional Information Work Style: Remo