Staff Software Engineer, AI & Recommendations Platform
Hims & Hers2 weeks ago
Location
US Remote
Workplace
Remote
Type
Full Time
Salary
USD 195,000 – 240,000
Level
Staff
Role
Staff Engineer
Posted
Jun 18, 2026
Full TimeRemoteStaff
The role
Summary
Staff Software Engineer on Hims & Hers' AI & Recommendations Platform team, responsible for designing and leading scalable backend systems, ML integration infrastructure, and experimentation platforms that power personalized treatment recommendations and provider decision support across multiple healthcare verticals. This role requires 5+ years of production systems experience with deep expertise in distributed systems, Python, and ML productionization, combining technical leadership with hands-on architecture and implementation across the full stack.
What you'll do
Backend Systems Architecture & Leadership: Lead the design, development, and maintenance of scalable backend systems, APIs, and platform services that power personalized treatment recommendations and patient/provider personalization engines. Architect highly reliable, observable, and maintainable distributed systems capable of handling healthcare-scale data volumes while ensuring HIPAA compliance and operational excellence.
ML Productionization & AI Integration: Partner with Machine Learning engineers to operationalize predictive models and integrate intelligent decisioning into customer-facing and provider workflows. Design and implement production pipelines that enable real-time model serving, continuous evaluation, and safe model deployment across multiple healthcare verticals with rigorous monitoring and rollback capabilities.
Experimentation Platform Infrastructure: Architect and build the core infrastructure enabling experimentation, A/B testing frameworks, and ML-powered healthcare experiences. Develop unified decisioning frameworks that allow independent teams to safely experiment with treatment recommendations, personalization algorithms, and clinical decision support systems while maintaining data integrity and audit trails.
Platform Investment & Self-Service Tooling: Drive strategic platform investments including self-service developer tooling, comprehensive testing infrastructure, data pipeline orchestration, and reusable ML integration patterns. Build capabilities that enable vertical engineering teams to independently innovate while adhering to company-wide standards for reliability, observability, and compliance.
Emerging Technology Evaluation & Integration: Evaluate and strategically integrate cutting-edge AI and Large Language Model technologies where they demonstrably improve provider efficiency, enhance patient outcomes, or enable operational scale. Lead proof-of-concepts and production implementations of LLM-powered applications including clinical documentation assistance, treatment optimization, and provider decision support systems.
Cross-Team Technical Leadership & Mentorship: Lead complex technical initiatives spanning multiple engineering teams and systems, establishing architectural patterns and best practices across the AI & Recommendations platform ecosystem. Mentor engineers through rigorous design reviews, architecture discussions, hands-on implementation support, and knowledge transfer to develop the next generation of platform leaders.
Strategic Technical Direction & Influence: Influence the long-term technical vision of the MedMatch platform and broader AI infrastructure. Collaborate with engineering, product, data science, and clinical stakeholders to align technical strategy with business objectives, ensuring the platform scales efficiently, maintains clinical integrity, and adapts to evolving healthcare market demands.
What we look for
Technical
Backend Systems & Distributed Architecture5+ years of professional software engineering experience designing, building, and operating production-grade backend systems at scale. Deep expertise in distributed systems design patterns, API architecture, cloud-native architectures, microservices patterns, and modern infrastructure as code approaches required.
Python Proficiency & Modern Development PracticesStrong proficiency in Python with demonstrated expertise in modern software development practices including design patterns, testing strategies, CI/CD pipelines, code review culture, and collaborative engineering workflows. Experience architecting maintainable, well-documented codebases for large engineering organizations.
Machine Learning Integration & ProductionizationDemonstrated success integrating ML models, recommendation engines, and LLM-powered applications into production systems. Familiarity with ML lifecycle concepts including model training, evaluation metrics, deployment strategies, ongoing monitoring, retraining triggers, experimentation frameworks, and failure handling.
Data-Intensive Application DevelopmentProven experience building data-intensive applications and services that leverage machine learning or sophisticated recommendation systems. Ability to design systems that efficiently handle feature engineering, real-time model serving, batch processing, and complex data transformations at production scale.
Cloud Platforms & Infrastructure ToolingHands-on experience with modern cloud platforms (AWS, Google Cloud, or Azure preferred) and infrastructure tooling including Kubernetes orchestration, container technologies (Docker), data platforms (Databricks, Snowflake), ML workflow tools (MLflow, Airflow), and observability solutions for production systems.
Education
Bachelor's Degree in Computer Science, Software Engineering, or Related FieldBachelor's degree (or equivalent professional experience) in Computer Science, Software Engineering, Mathematics, Physics, or related discipline demonstrating strong foundational knowledge in algorithms, data structures, and system design principles.
Advanced Degree in Computer Science, Machine Learning, or Related Field (Preferred)Master's degree or PhD in Computer Science, Machine Learning, Data Science, or related field is a significant asset, providing deeper expertise in ML theory, statistical methods, and advanced system design concepts applicable to AI platform development.
Experience
Production Systems Leadership5+ years building, deploying, and operating production systems that serve millions of users or handle significant data volumes. Demonstrated ability to balance immediate product delivery with long-term platform scalability, maintainability, and technical debt management in complex organizations.
Large Technical Initiatives & Cross-Team InfluenceProven track record leading large-scale technical initiatives that span multiple teams and systems, with demonstrated influence over architectural decisions. Experience establishing platform standards, design patterns, and best practices that enable independent team innovation while maintaining systemic integrity.
Recommendation & Personalization SystemsExperience building or operating recommendation systems, ranking algorithms, personalization platforms, or decision-support systems in production environments. Understanding of collaborative filtering, content-based approaches, hybrid methodologies, or ranking optimization techniques is highly valuable.
Healthcare or Regulated Environment EngineeringExperience working in healthcare, health technology, or other highly regulated environments with compliance requirements (HIPAA, GDPR, SOC 2). Understanding of audit trails, data governance, privacy-by-design principles, and clinical validation processes strengthens candidacy significantly.
Skills
Required skills
Distributed Systems DesignAdvanced ability to design, implement, and troubleshoot distributed systems handling high concurrency, fault tolerance, eventual consistency, and scalability challenges. Expertise in patterns like event sourcing, CQRS, saga patterns, and load balancing.
Python Backend DevelopmentExpert-level Python proficiency with deep understanding of async programming, web frameworks (FastAPI, Django), testing frameworks, performance optimization, and production debugging. Ability to architect Python systems for scale.
API Design & Platform ArchitectureExpertise designing RESTful and event-driven APIs that enable platform integration, versioning strategies, backward compatibility, rate limiting, and documentation for developer experience. Understanding of API-first architecture patterns.
ML Model Deployment & ServingHands-on experience deploying machine learning models to production, implementing real-time serving infrastructure, batch processing pipelines, model versioning strategies, and handling model performance degradation and retraining.
Cloud Infrastructure & Container OrchestrationStrong expertise with cloud platforms (AWS preferred for Hims context) and containerization technologies. Proficiency with Kubernetes, infrastructure-as-code (Terraform/CloudFormation), monitoring, logging, and cost optimization.
Data Pipeline & Workflow OrchestrationExperience designing and implementing data pipelines using orchestration tools like Airflow or Dagster. Expertise in ETL design, schema management, data quality validation, and lineage tracking for complex data workflows.
System Design & ScalabilityDemonstrated mastery in designing systems for scale, including database selection (relational vs. NoSQL), caching strategies, asynchronous processing, data partitioning, and capacity planning for production workloads.
Observability & Production DebuggingExpert-level understanding of observability principles including structured logging, distributed tracing, metrics collection, and alerting. Proven ability to diagnose and resolve production issues in complex systems.
Technical Leadership & CommunicationExceptional ability to communicate complex technical concepts to diverse audiences including engineers, product managers, and non-technical stakeholders. Proven mentoring and architectural guidance capabilities.
Experimentation & A/B Testing FrameworksUnderstanding of statistical rigor in A/B testing, experimental design for recommendation systems, controlling for confounding variables, and interpreting results in healthcare contexts where multiple metrics may conflict.
Nice to have
LLM Integration & Prompt EngineeringExperience integrating Large Language Models (OpenAI, Anthropic, open-source models) into production systems, including prompt engineering, RAG (Retrieval-Augmented Generation) implementations, fine-tuning strategies, and managing LLM inference costs at scale.
Recommendation System ImplementationHands-on experience building collaborative filtering, content-based filtering, or hybrid recommendation systems. Knowledge of ranking algorithms, diversity/serendipity in recommendations, cold-start problems, and recommendation system evaluation metrics.
Healthcare Data & Clinical ConceptsFamiliarity with healthcare data formats (HL7, FHIR), clinical terminology, medical ontologies (SNOMED, ICD), and understanding of healthcare workflows, provider-patient interactions, and clinical decision-support system design principles.
Feature Stores & ML PlatformsExperience with feature management systems, ML platform infrastructure, model registries, and ML workflow automation. Familiarity with tools like Feast, Tecton, or custom feature platforms for managing ML features at scale.
Real-Time Systems & Stream ProcessingExperience with real-time data processing frameworks (Apache Kafka, Flink, Spark Streaming) for building low-latency personalization systems, real-time recommendations, and event-driven architectures.
Privacy & Compliance EngineeringUnderstanding of privacy engineering practices (differential privacy, federated learning), compliance requirements (HIPAA, GDPR), data governance frameworks, and secure multi-party computation for healthcare applications.
Graduate-Level Machine Learning TheoryAdvanced understanding of machine learning theory, statistical foundations, optimization algorithms, and research papers in recommendation systems and causal inference. Experience with academic research or publishing in ML domains.
Cross-Functional Product DevelopmentDemonstrated ability working effectively across engineering, data science, product management, clinical affairs, and business stakeholders to translate requirements into technical solutions with business impact.
Compensation & benefits
Salary
USD 195,000 – 240,000 (annual)
Stock options
Available
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Hims & Hers is a digital health company that provides affordable and convenient healthcare services to patients through a direct-to-consumer online platform. The company offers prescription medications, treatments, and consultations with licensed healthcare providers for a wide range of common conditions including erectile dysfunction, hair loss, and skincare concerns. Hims & Hers operates in the telehealth and e-commerce pharmacy market, making healthcare more accessible by eliminating traditional barriers like lengthy doctor visits and expensive in-person consultations. The company is on a mission to make healthcare more affordable, accessible, and transparent for everyday people.
San Francisco, USAFounded 2017hims.com
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