Staff Machine Learning Engineer

People In AI; United States; Remote; USD 200,000 - 210,000; Full time

Job summary

Join a leading healthcare revenue recovery platform as a Staff Machine Learning Engineer focused on causal inference. This role involves designing ML systems that optimize claims workflows and impact revenue recovery, within a trusted remote-first environment, fostering a culture of trust and results.

Benefits

Medical insurance

Child care support

Paid maternity leave

Paid paternity leave

401(k)

Vision insurance

Pension plan

Qualifications

• 8–12+ years of industry experience in applied ML.
• Proven track record of deploying ML models in production.
• Experience with causal inference frameworks.

Responsibilities

• Design and productionize models for claims handling.
• Lead causal inference initiatives.
• Own end-to-end model lifecycle.

Skills

Causal reasoning

Experimentation

Machine learning

Python

Statistical intuition

Education

Master's or PhD in related field

Tools

scikit-learn

PyTorch

AWS

GCP

Job description

This range is provided by People In AI. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$200,000.00/yr - $210,000.00/yr

Direct message the job poster from People In AI

Placing the Top AI and Machine Learning Talent

Staff Machine Learning Engineer, Causal Inference & Workflow Optimization

$200,000 - $210,000 base + 10% bonus

Remote-first (30+ approved US states)

Join a leading healthcare revenue recovery platform revolutionizing how hospitals reclaim underpaid and denied claims through machine learning and intelligent automation.

About the Company

This PE-backed healthcare data company processes over 100 million hospital claims annually, unlocking nearly $1B in recovered revenue. With a 25+ year legacy and a mandate to modernize its analytics infrastructure, the company is embarking on an ambitious AI transformation—infusing causal inference, ML-powered decision systems, and experimentation into every facet of its operation.

The Role

You’ll take end-to-end ownership of ML systems that directly impact operational efficiency and revenue recovery. This is a greenfield opportunity to design causal-first frameworks that prioritize, route, and optimize healthcare claims workflows—transforming brittle, rules-based systems into adaptive, intelligent engines.

You'll join a high-caliber, fast-growing ML team embedded directly into business-critical decisions. Expect rich datasets, real-world signal-to-noise challenges, and the chance to influence strategy, tooling, and research direction.

What You’ll Do

• Design and productionize models that rank, route, and optimize claims handling
• Lead causal inference initiatives—developing frameworks for uplift modeling, counterfactual prediction, and treatment effect estimation
• Partner with operations and product to build systems that learn from claim outcomes and analyst behaviors
• Prototype and evaluate matching algorithms, recommendation engines, and exploration-exploitation strategies
• Own end-to-end model lifecycle: from hypothesis formation and feature engineering to deployment and monitoring
• Shape the team’s best practices around experimentation, statistical rigor, and impact measurement

What You’ll Bring

• 8–12+ years of industry experience in applied ML, with a focus on causal reasoning or experimentation
• Proven track record of deploying ML models in production, especially within high-noise, high-scale environments
• Deep statistical intuition and comfort navigating ambiguity using structured, hypothesis-driven thinking
• Strong Python and ML libraries (e.g., scikit-learn, PyTorch, Hugging Face); production mindset a must
• Experience with causal inference frameworks (e.g., DoWhy, EconML) or designing experiments in noisy real-world systems
• Bonus: Spark, cloud ML infra (AWS/GCP), or prompt engineering familiarity
• Causal inference libraries (e.g., EconML, DoWhy)

Why Join?

• Causal-first charter: help lead a shift from rules to rigorously reasoned decisions
• Greenfield ML: pilot models already in use—huge room to scale and expand
• Trusted domain experts: direct mentorship from leadership with deep causal expertise
• Strategic impact: model outcomes are mission-critical and highly visible
• Balanced culture: remote-first with a focus on trust, autonomy, and results

About People In AI

People In AI is a specialized recruitment firm connecting world-class talent with the most ambitious teams in machine learning and applied AI. We work with startups and scale-ups who are shaping the future with intelligent systems—and we’re here to make sure the right people lead the way.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering, Production, and Strategy/Planning

• Industries

  Hospitals and Health Care, Retail, and Space Research and Technology

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Inferred from the description for this job

Medical insurance

Child care support

Paid maternity leave

Paid paternity leave

401(k)

Vision insurance

Pension plan

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