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Closed listing

Humana

Senior Decision Intelligence Engineer

Washington, DC, United States

$106,900 - $147,000 per year

This opening has closed.

The original listing is kept below for reference.

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About employer

Job details

Pay: $106,900 - $147,000 per year
Experience: 2+ years of hands-on experience implementing reinforcement learning, operations research methods, or simulation-driven decision systems in production.
Working hours: 40 hours per week
Posting language: English
Posted: Sep 14, 2026

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Original job description

Archived listing. The details below describe a past opening.

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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: Remote/Hybrid - Preferably Boston, MA.

Occasional travel to Humana's offices for training or meetings may be required.

Work Hours : Typical business hours are Monday-Friday, 8 hours/day, 5 days/week-- some flexibility might be possible, depending on business needs.

Very minimal travel might be required for training, meetings, and/or conferences

Interview Format

As part of our hiring process, we will be using on-demand technology provided by Hire Vue, a third-party vendor. This technology provides our team of recruiters and hiring managers with an enhanced method for decision-making through on-demand candidate assessments.

If you are selected to move forward from your application prescreen, you will receive correspondence inviting you to participate in an on-demand assessment with pre-determined questions. You should anticipate the assessment to take approximately 10-15 minutes.

Your on-demand assessment will be reviewed, and you will subsequently be informed if you will be moving forward to next round of interviews.

Work at Home Requirements: To ensure Home or Hybrid Home/Office employees’ ability to work effectively, the self-provided internet service of Home or Hybrid Home/Office employees must meet the following criteria: At minimum, a download speed of 25 Mbps and an upload speed of 10 Mbps is required; wireless, wired cable or DSL connection is suggested. In certain roles, the minimum recommended internet speed required by Humana may not be sufficient for business needs. Humana reserves the right to require associates to upgrade their internet service if necessary. Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information.

Travel: While this is a remote position, occasional travel to Humana's offices for training or meetings may be required.

Scheduled Weekly Hours

Pay Range

The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc.

$106,900 - $147,000 per year

This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.

Description of Benefits

Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole-person well-being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. Among our benefits, Humana provides medical, dental and vision benefits, 401(k) retirement savings plan, time off (including paid time off, company and personal holidays, paid parental and caregiver leave), short-term and long-term disability, life insurance and many other opportunities.

Application Deadline: 10-29-2026

About us

About Humana: Humana Inc. (NYSE: HUM) is a leading U.S. healthcare company. Through our Humana insurance services and our CenterWell healthcare services, we make it easier for the millions of people we serve to achieve their best health – delivering the care and service they need, when they need it. These efforts are leading to a better quality of life for people with Medicare and Medicaid, families, individuals, military service personnel, and communities at large. Learn more about what we offer at Humana.com and at CenterWell.com.

Equal Opportunity Employer

It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status. It is also the policy of Humana to take affirmative action, in compliance with Section 503 of the Rehabilitation Act and VEVRAA, to employ and to advance in employment individuals with disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.

Humana complies with all applicable federal civil rights laws and does not discriminate on the basis of race, color, national origin, age, disability, sex, sexual orientation, gender identity or religion. We also provide free language interpreter services. See our https://www.humana.com/legal/accessibility-resources?source=Humana_Website.

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More about this job

Skills

ML
Computer science
Research
Estimating
Python
PyTorch
TensorFlow
Databricks
PySpark
Mlflow
Vue.js
Decision making

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