Job Overview
Location
San Francisco
Job Type
Full-time
Category
Software Engineering
Date Posted
June 4, 2026
Full Job Description
đź“‹ Description
- • Design and ship end-to-end ML systems including data pipelines, model training, evaluation, and production deployment for enterprise-grade AI applications.
- • Own and optimize model performance, latency, and cost trade-offs in live production environments to ensure scalability and reliability.
- • Build and maintain evaluation harnesses and offline benchmarks to enable rapid iteration and quantifiable model improvements.
- • Collaborate directly with product teams to translate ambiguous business goals into measurable, data-driven model objectives.
- • Implement and scale inference serving infrastructure to support high-throughput, low-latency AI predictions for enterprise customers.
- • Develop and maintain robust training pipelines that handle large-scale datasets and complex model architectures, including LLMs and recommender systems.
- • Optimize GPU utilization and distributed training workflows to reduce training time and infrastructure costs.
- • Lead code reviews and mentor junior engineers on ML engineering best practices, model versioning, testing, and production-grade code quality.
- • Ensure model evaluation systems are aligned with business KPIs and provide actionable insights for iterative model refinement.
- • Balance research-grade innovation with ship-grade engineering constraints to deliver production-ready AI solutions under tight timelines.
- • Work closely with infrastructure and data teams to integrate model pipelines with existing enterprise data ecosystems and monitoring tools.
- • Document system architecture, model behavior, and performance metrics to support auditability, compliance, and cross-team knowledge sharing.
- • Proactively identify bottlenecks in the ML lifecycle and implement automated solutions for data validation, model drift detection, and retraining triggers.
- • Participate in on-call rotations to ensure system reliability and rapid response to production incidents affecting model performance.
- • Stay current with advancements in LLMs, RAG, and large-scale inference systems to continuously improve the platform’s capabilities.
🎯 Requirements
- • 4+ years of applied ML engineering in production environments
- • Hands-on experience with LLMs, fine-tuning, RAG, or large-scale recommender systems
- • Strong Python and PyTorch (or JAX) fundamentals
- • Experience with distributed training, GPU optimization, or inference serving
- • Pragmatic about trade-offs between research-grade and ship-grade work
🏖️ Benefits
- • Full-time, on-site position in San Francisco
- • Work with a well-funded AI startup building production-grade ML infrastructure for enterprise customers
- • Opportunity to own critical ML systems from end-to-end
- • Direct collaboration with product teams to drive measurable business impact
- • Mentorship opportunities for junior engineers
- • Exposure to cutting-edge LLM and large-scale AI technologies
Skills & Technologies
About Clera
Clera, Inc. operates as an AI Talent Agent, leveraging artificial intelligence to enhance and streamline various aspects of the talent acquisition and management process. The company's core offering appears to center on an AI-powered platform designed to connect skilled individuals with suitable opportunities or to assist organizations in efficiently sourcing and evaluating candidates. While comprehensive details regarding specific features, target industries, or the scale of its operations are not yet publicly available, the current online presence indicates that Clera's services are actively being prepared for launch. This suggests an upcoming introduction of innovative AI solutions aimed at optimizing the ecosystem where talent meets demand.
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