Applied AI Engineer

Salt Digital Recruitment; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Salt Digital Recruitment seeks an Applied AI Engineer to turn model capabilities into reliable, production-ready product behavior. You will own problems end-to-end, from shaping model behavior and designing AI workflows through to building surrounding systems for production reliability.

This role sits at the intersection of machine learning, systems engineering and product, focused on real-user impact. You will work with product, engineering and research teams to translate ambiguous problems,

Qualifications

• Strong foundation in machine learning and modern neural network architectures.
• Hands-on experience training, fine-tuning and deploying ML models.
• Experience with Large Language Models (LLMs) and generative AI systems.

Responsibilities

• Build and ship AI-powered features end-to-end.
• Design, test and improve prompts, memory systems and agent workflows.
• Transform raw model outputs into structured, reliable product behaviors.
• Debug issues across the AI stack, including models, orchestration, infrastructure and user experience.
• Optimize AI systems for latency, cost, performance and production reliability.
• Collaborate with product, engineering and research teams to translate ambiguous problems into working solutions.

Skills

Machine learning

LLMs experience

Production-quality code

Problem solving

Shipping mindset

Tools

Python

PyTorch

JAX

Vector databases

Model serving

Job description

About the Opportunity

We are partnering with a fast-growing technology company developing a new generation of AI-native applications designed to make everyday tasks, communication, organization and workflows more intelligent and intuitive. The team is building proactive AI experiences with a strong focus on persistent context, reliable long-running workflows and successful real-world task completion. They are looking for an Applied AI Engineer to bridge the gap between advanced model capabilities and real-world product experiences.

About the Role

As an Applied AI Engineer, you will turn model capabilities into reliable, production-ready product behavior. You will own problems end-to-end, from shaping model behavior and designing AI workflows through to building the surrounding systems and ensuring they perform effectively in production. Sitting at the intersection of machine learning, systems engineering and product, this role is focused on making AI work for real users - not simply building models or demonstrations.

What You'll Be Doing

Build and ship AI-powered features end-to-end, from model to system to user experience. Design, test and continuously improve prompts, tools, memory systems and agent workflows. Transform raw model outputs into structured, reliable and predictable product behaviors. Build and improve agentic systems capable of interacting with tools, data and external services. Debug issues across the complete AI stack, including models, orchestration, infrastructure and user experience. Optimize AI systems for latency, cost, performance and production reliability. Develop lightweight evaluation frameworks to measure real-world model and system performance. Identify model and system failure modes and develop mechanisms to improve reliability. Work closely with product, engineering and research teams to translate ambiguous problems into working solutions. Continuously iterate on models and systems using production data, user behavior and measurable performance signals.

What We're Looking For

Strong foundation in machine learning and modern neural network architectures. Hands-on experience training, fine-tuning and/or deploying machine learning models. Practical experience working with Large Language Models (LLMs) and modern generative AI systems. Experience building AI-powered applications or features that have been deployed into production. Ability to write clean, maintainable and production-quality code. Comfortable working across multiple abstraction layers, from models and infrastructure through to product. Understanding of inference, model serving and production ML considerations. Strong problem-solving skills and the ability to navigate ambiguous technical challenges. Ability to make pragmatic engineering decisions in a fast-moving environment. A strong bias toward shipping, experimentation, iteration and continuous improvement.

Technology Environment

• Python


• PyTorch


• JAX


• Large Language Models (LLMs)


• Commercial and open-source model APIs


• LLaMA


• Qwen vLLM or similar inference/serving technologies


• Vector databases


• Model training and fine-tuning


• AI agents and tool calling


• Memory and context systems


• Model evaluation frameworks


• Production ML infrastructure

What Success Looks Like

AI and machine learning capabilities will successfully translate into reliable, production-ready product experiences that meet defined accuracy, latency and reliability targets. Production issues will be quickly identified, debugged and resolved at their root cause, whether they originate within the model, orchestration layer, infrastructure or product experience. Data pipelines, training processes and inference systems will remain robust, reproducible and maintainable as the product scales. You will work closely with engineering, product and research teams to ensure improvements are driven by real-world signals and measurable outcomes, continuously increasing the performance and reliability of AI-powered features.