Senior Machine Learning EngineerHarrison Clarke • San Francisco, CA, United States
Senior Machine Learning Engineer
Harrison Clarke • San Francisco, CA, United States
[job_card.variable_days_ago]
[job_preview.job_type]
- [job_card.full_time]
[job_card.job_description]Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, or a related technical field. 2+ years of professional experience in software engineering, platform engineering, or ML engineering. Demonstrated experience building and shipping AI systems that leverage LLMs and agentic / tool‑calling patterns. Strong background in ML pipelines, data engineering for model training, and vector / representation stores or equivalents. Proficiency in Python and common ML frameworks and tooling. Excellent problem‑solving skills and experience working in fast‑paced, collaborative teams. Experience deploying production‑grade, large‑scale AI applications with attention to reliability and cost. Familiarity with model evaluation, observability, A / B testing, and post‑deployment monitoring for AI. Strong communication skills for translating technical tradeoffs to non‑technical stakeholders. The opportunity to build systems that materially improve how businesses operate using AI. High ownership and a direct influence on product and architecture decisions. A collaborative engineering culture focused on impact, quality, and rapid iteration.
About the Company
One of our clients is a fast-moving AI engineering team building next-generation multi-agent systems that automate complex business workflows. Their focus is on creating scalable, secure AI agents that integrate with enterprise software and drive measurable operational impact. This role is an opportunity to build production-grade AI systems at scale and influence the architecture of agentic workflows used in real business settings.
Role Overview
They are seeking an experienced AI Agent Engineer who specializes in large language models (LLMs), agent architectures, and tool-enabled AI systems. You will design, implement, and deploy multi-step agent pipelines, build robust ML pipelines for model training and fine-tuning, and manage inference infrastructure. This role blends ML research, systems engineering, and product-oriented delivery.
Key Responsibilities
- Design and implement complex, multi-step agent pipelines using both open- and closed-source LLMs.
- Build tools and adapters that allow agents to interact securely with enterprise applications and APIs.
- Ensure agent behavior aligns with business objectives, reliability targets, and relevant compliance requirements.
- Create data generation, processing, and labeling pipelines to support model training and fine-tuning.
- Implement training and evaluation workflows for LLMs and downstream agent components.
- Run experiments to improve agent reasoning, tool-use, and task completion metrics.
- Operate and optimize inference endpoints and related serving infrastructure on cloud platforms.
- Implement monitoring, observability, and performance testing for deployed models and agents.
- Contribute to secure, scalable CI / CD for model release and rollbacks.
Required Qualifications
Preferred Qualifications
What They Offer
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Engineering and Research
Industries
Software Development
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