Member of Technical Staff
Listed by employer · Checked
Older posting · still listed ·
- Work arrangement
- onsite
- Employment
- FullTime
- Stated compensation
- USD 180,000–230,000 1 YEAR
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Requirements
1 with evidence8 unclear or unstated- Remote policy
- onsite
“Provider workplace type: onsite”
From employer posting metadata - Employment type
- FullTime
- Compensation
- USD 180,000–230,000 1 YEAR
8 fields not stated in this posting
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Degree requirement
- Not specified
- Security clearance
- Not mentioned
- Timezone / hours
- Not specified
- Citizenship requirement
- Not mentioned
- Minimum experience (years)
- Not specified
- Work authorization
- Not specified
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Original job description
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Job Description
We’re looking for a Member of Technical Staff to build and deploy production-grade AI systems. In this role, you’ll work across modeling, systems, and product to take ideas from concept to real-world applications.
Key Responsibilities
System Development & Deployment: Build, integrate, and deploy AI-powered systems into production environments
Model Development: Fine-tune, evaluate, and work with machine learning models in real-world applications
Systems Engineering: Design scalable pipelines for training, inference, and data processing
Performance Optimization: Improve latency, throughput, cost efficiency, and reliability of systems
Data & Infrastructure: Work with large-scale datasets and integrate systems with internal tools and APIs
Cross-Functional Collaboration: Partner with product, research, and design to ship end-to-end features
Evaluation & Monitoring: Implement evaluation frameworks, observability, and feedback loops
Minimum Qualifications
Education: Bachelor’s or Master’s in Computer Science, Engineering, or related field
Technical Skills: Strong proficiency in Python and modern engineering or ML frameworks
Production Experience: Experience building and deploying systems in production environments
Systems Knowledge: Familiarity with data pipelines, APIs, and cloud infrastructure (AWS, GCP)
Practical ML Experience: Experience working with machine learning models or data-driven systems
Nice to Have
Experience deploying or scaling ML systems in production
Familiarity with LLMs, agents, or workflow automation systems
Experience with distributed systems or large-scale infrastructure
Background in fast-paced or early-stage environments
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