ML Engineer (Medtech)

TL;DR

ML Engineer (Medtech): Building and hardening production systems for training, deploying, and monitoring machine learning models with an accent on reliability, scalability, and system integration. Focus on automating retraining pipelines, ensuring model performance, and building clean interfaces between data, models, and product systems.

Location: Must be based in or able to commute to San Francisco, CA (Hybrid: Monday–Thursday in-office).

Salary: $140,000 – $200,000 + Equity.

Company

hirify.global is a healthcare technology company reimagining patient access by delivering care directly to homes, backed by top-tier investors like a16z and General Catalyst.

What you will do

• Build and harden training and inference pipelines.
• Serve predictions through APIs and batch jobs with a focus on reliability.
• Monitor drift, data quality, latency, and model performance.
• Automate retraining, validation, and safe rollback processes.
• Productionize models handed off from research teams.
• Implement reproducibility, versioning, and model-governance artifacts.

Requirements

• Must be able to work from the San Francisco office Monday through Thursday.
• Strong Python and software engineering fundamentals.
• Experience with ML frameworks, data pipelines, and model serving.
• Proven ability to take models from prototype to reliable production.
• Experience with cloud infrastructure, containers, CI/CD, and orchestration.
• Knowledge of monitoring, observability, reproducibility, and versioning.

Nice to have

• Background in backend, data, or platform engineering.
• Experience with feature stores at scale.
• Familiarity with healthcare data and PHI-aware systems.

Culture & Benefits

• Medical, dental, and vision plans 100% paid for employees and dependents.
• 401(k) with company match and HSA/FSA contributions.
• 16-week parental leave for birthing parents and 8 weeks for others.
• Flexible PTO and 10 paid holidays per year.
• Free daily lunch in-office with team connection time.
• Annual learning stipend and meaningful pre-IPO equity.

Hiring process

• Recruiter screen, hiring manager intro, and technical assessment.
• Onsite interview including systems design, case study, and behavioral interview.