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Staff ML Engineer
Press Ganey. Lead the design, delivery, and operation of production-grade AI systems .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading the design and operation of production-grade AI systems, with a strong focus on Python, SQL, and high-throughput distributed systems. Proven ability to mentor teams, optimize AI services, and ensure compliance with data privacy and governance standards.
Highest-signal resume keywords
Python ProficiencyML/LLM Systems OwnershipHigh-Throughput Distributed Systems DesignCI/CD and Automated Testing PracticesTechnical Leadership and Mentorship
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLMachine LearningLarge Language ModelsDistributed SystemsAutomated TestingCI/CDMonitoringIncident ResponseRoot-Cause Analysis
Soft Skills
Technical CommunicationMentorshipCross-Team Collaboration
Tools & Technologies
DatabricksAWSAzureNLPText Analytics
Industry Keywords
Data PrivacyModel GovernanceHealthcare ComplianceSensitive Data Handling
Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsPythonSQL
About the role
Key responsibilities & impact- Lead the design, delivery, and operation of production-grade AI systems
- Build services and high-throughput pipelines for a text analytics platform and AI products
- Own technical delivery from prototype through deployment and ongoing production support
- Partner with AI Scientists and Product to define requirements, plan implementation, and resolve cross-team dependencies
- Evaluate AI solutions for production suitability and identify technical risks
- Choose architectures meeting quality, reliability, and cost requirements
- Architect and build high-volume AI services and processing pipelines with fault tolerance, backpressure, retries, idempotency, and partial-failure recovery
- Lead the evolution of Python services and a Databricks-based platform for distributed processing, model serving, and ML/LLM integration
- Establish standards for automated testing, CI/CD, model and prompt versioning, load testing, controlled rollouts, and rollback
- Build evaluation and monitoring capabilities for AI quality regressions and service reliability, throughput, latency, and inference cost
- Partner with Product and Responsible AI teams on release criteria, model validation, data privacy, security, and governance
- Optimize processing and inference workloads for quality, throughput, latency, capacity, and cost
- Mentor engineers and lead architecture and code reviews
Requirements
What you’ll need- Bachelor's degree in Computer Science, Electrical Engineering, or a related technical discipline, or equivalent practical experience
- 8+ years of professional software engineering experience
- At least 3 years owning ML or LLM systems in production and their operational support
- Proven ability to independently lead complex technical initiatives from requirements through production
- Advanced proficiency in Python for production services and data processing
- Strong SQL skills
- Experience designing and operating high-throughput distributed systems
- Strong understanding of failure recovery, multi-tenancy, and capacity planning
- Hands-on experience deploying and operating LLM-based applications, including evaluation, output validation, observability, and cost management
- Strong production engineering practices across automated testing, CI/CD, monitoring, incident response, and root-cause analysis
- Demonstrated technical leadership through system design, hands-on implementation, code review, and mentorship
- Ability to communicate technical decisions and tradeoffs clearly to engineering, research, product, and governance stakeholders
- Preferred: Experience with Databricks or comparable cloud-based data and AI platforms
- Preferred: Experience with NLP, text analytics, or large-scale processing of unstructured data
- Preferred: Experience building shared infrastructure for inference, evaluation, and model lifecycle management
- Preferred: Familiarity with retrieval-augmented generation, semantic search, and LLM orchestration frameworks
- Preferred: Experience with speech-to-text, speaker diarization, or conversational audio processing
- Preferred: Experience deploying and operating cloud-native services on AWS or Azure
- Preferred: Experience with healthcare or other regulated environments, including sensitive data handling, auditability, and model governance
Benefits
Comp & perks- Competitive benefits package
- Discretionary bonus or commission tied to achieved results
- Reasonable accommodations for qualified individuals with disabilities or disabled veterans during the hiring process