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About This Role
For People is a team of skilled technologists improving government digital services for disadvantaged and vulnerable populations. We embed directly in government agencies to modernize software, systems, and platforms so that they better serve people.
Your Impact
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As the Data Science Engineering Manager for our Medicaid programs, you will directly influence the quality of care for millions of vulnerable Americans. By nurturing a guild of data scientists and serving as the hands\-on technical anchor for one of our data science programs, you will transform fragmented healthcare data into actionable insights for our government partners. Ultimately, your leadership and technical stewardship will bridge the gap between public health policy and data engineering, strengthening the integrity of our nation's Medicaid program and helping the most vulnerable populations.
Our Culture
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For People is a team of humans. We place a significant amount of emphasis on positive work\-life balance, setting healthy expectations, and making sure our loved ones are taken care of first. That means picking a child up from school during the day or going for a mid\-day walk is okay!
This position is 100% remote. Our entire team is remote across the United States, from the West Coast to the East Coast. There will never be a return\-to\-office, as we have none!
This position's published base salary range is between $150,000 and $165,000 annually, plus generous benefits (e.g., For People pays 100% of Gold\-tier employee health insurance premiums) and annual company profit sharing.
Your Opportunities
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At For People, you will:
- Lead, mentor, and grow a team of 5\-10 data scientists by setting clear expectations, providing regular feedback, championing professional development, and fostering a collaborative guild culture
- Act as the daily technical lead for a smaller Medicaid data science program with 2 other data scientists by removing blockers, managing timelines, prioritizing sprint work, and ensuring the team hits critical project milestones
- Guide the technical direction of data science workstreams, ensuring the team delivers scalable, high\-quality, well\-documented, and testable code
- Design, write, and review SQL queries, Python scripts, and PySpark data transformations to extract and manipulate government Medicaid datasets
- Partner with Data Engineering teams to coordinate the build, testing, and deployment of automated ETL data pipelines within an AWS\-hosted Databricks lakehouse environment
- Act as the primary technical point of contact for business analysts, internal leadership, and government stakeholders
- Develop and present reporting mechanisms that provide actionable insights back to the client and internal leadership
You Bring
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- A humble and caring attitude aligning with For People’s values– how we work with passion, fun, curiosity and sustainability, humility and respect
- 8\+ years of hands\-on experience as a data scientist or data engineering professional, with at least 2 years operating in a people management, practice lead, or technical lead capacity
- Direct experience working with health insurance data, such as Medicaid/Medicare claims or similar health datasets
- Expert\-level proficiency in SQL for querying large datasets, alongside strong Python and PySpark programming skills for advanced data analysis, transformation, and automation
- Hands\-on data engineering experience working within Databricks and modern cloud platforms to build and manage data pipelines
- Familiarity with modern software engineering practices, including Git version control, code reviews, and CI/CD pipelines
- Proven ability to create highly detailed technical documentation
- Excellent communication skills with a demonstrated ability to translate complex technical concepts for non\-technical stakeholders, including government clients
The following elements are not required, but nice to have:* Experience building dashboards and visualizations using BI tools such as PowerBI, AWS QuickSight, or Tableau.
- Strong foundational understanding of data warehousing principles, ETL design, and data structure/modeling best practices
Additional Details
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You will be working on a United States government platform, and they have a few basic requirements for contractors like ourselves. You must perform all work physically within the United States at all times. In addition, you must be a United States citizen and be able to pass a government\-performed public trust background check.
For People is an Equal Employment Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, and/or veteran status.
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Salary Context
This $150K-$165K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At for people, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($157K) sits 6% below the category median. Disclosed range: $150K to $165K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
for people AI Hiring
for people has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $165K - $165K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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