Applied AI Engineer

USA Tech Recruit; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

USA Tech Recruit is seeking an ML/AI Engineer to help expand in the US, starting with several hires in the SF area. You will work on a proactive AI assistant that performs multi-step tasks with minimal prompting, leveraging Python, PyTorch/JAX, LLMs, and vector databases.

You will ship models and systems across model, infra, and product layers, collaborating with product and research teams to drive measurable improvements in accuracy, latency, and reliability.

Qualifications

• Strong foundation in machine learning and modern neural network architectures.
• Hands-on experience with training, fine-tuning, or deploying ML models.
• Ability to write clean, production-quality code.
• Comfort working across abstraction layers (model → infra → product).

Responsibilities

• ML models in production meet expected accuracy, latency, and reliability targets.
• Production issues are identified quickly, debugged effectively, and root causes addressed.
• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
• Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
• Iterations on models and systems are driven by real-world signals and measurable improvements.

Skills

ML fundamentals

Neural nets

Model deployment

Python

PyTorch

JAX

LLMs

Vector DB

Tools

PyTorch

JAX

vLLM

OpenAI APIs

Job description

A long-standing client is expanding into the US and Europe. Starting with several hires in the US. This will involve working on a proactive AI assistant rather than a traditional chatbot, with highly reliable AI capable of completing multi-step tasks with minimal user prompting

Ideal Experience

• Strong foundation in machine learning and modern neural network architectures.
• Hands-on experience with training, fine-tuning, or deploying ML models
• Ability to write clean, production-quality code
• Comfort working across abstraction layers (model ? infra ? product)
• Strong problem-solving skills in ambiguous, fast-moving environments
• Bias toward shipping, iteration, and continuous improvement
• Tech stack: Python, PyTorch / JAX, LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.), Inference/serving (e.g. vLLM), Vector DB

Outcomes

• ML models in production meet expected accuracy, latency, and reliability targets.
• Production issues are identified quickly, debugged effectively, and root causes addressed.
• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
• Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
• Iterations on models and systems are driven by real-world signals and measurable improvements

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