Staff AI/ML Engineer, Agents & Model Adaptation in Mundelein

Energy Jobline ZR; Mundelein (IL); Presencial; USD 225.000 - 275.000; Tempo integral

Resumo da vaga

Energy Jobline ZR is seeking a Staff Applied AI/ML Engineer to shape agent architecture and model strategy while staying hands-on. The role focuses on when frontier models or smaller adapted models are best, and how to prove production viability.

The position requires designing agent systems, selecting models, fine-tuning, and building evaluation harnesses. Onsite in New York City five days a week with equity compensation.

Qualificações

• Hands-on Python and PyTorch experience.
• Experience shipping agents that use external tools and produce structured outputs.
• Hands-on LLM fine-tuning with clear training choices and production outcomes.
• Ability to design evaluations to measure accuracy and reliability.

Responsabilidades

• Design and build agent systems with tool use, context management, orchestration, and guardrails.
• Choose models based on accuracy, latency, cost, and operational requirements.
• Fine-tune open-weight models using LoRA, QLoRA, supervised fine-tuning, and preference optimization.
• Turn user corrections and production traces into training data and evaluation cases.

Conhecimentos

Python

PyTorch

Agent systems

LLM fine-tuning

Model evaluation

Production ownership

Technical leadership

Evaluation design

Reliability

Descrição da vaga

Job Description

Staff Applied AI/ML Engineer, Agents & Model Adaptation

Compensation: $225K–$275K base + equity

Location: New York City | Five days onsite

A venture-backed AI company helping businesses investigate complex information and automate evidence-based workflows.

The company is building AI systems that connect fragmented records, identify relationships, and help people make better decisions. The engineering challenge is making agents accurate, reliable, and useful when the data is messy and mistakes carry real consequences.

As a Staff Applied AI Engineer, you’ll shape the agent architecture and model strategy while staying hands-on. You’ll help decide when frontier models are the right choice, when smaller adapted models make more sense, and how to prove that either approach works in production.

What You’ll Do

• Design and build agent systems spanning tool use, context management, orchestration, and guardrails.
• Choose models based on accuracy, latency, cost, and operational requirements.
• Fine-tune open-weight models using approaches such as LoRA, QLoRA, supervised fine-tuning, and preference optimization.
• Turn user corrections and production traces into training data and evaluation cases.
• Build evaluation harnesses that measure accuracy, hallucinations, false positives, and task completion.
• Own production serving decisions, including batching, quantization, monitoring, and inference efficiency.
• Test systems against prompt injection, data leakage, and adversarial inputs.
• Guide architecture, review designs, and mentor engineers while continuing to write code.

What You’ll Bring

• Software engineering or applied ML experience, including technical ownership of production AI systems.
• Experience shipping agents that use external tools and produce structured outputs.
• Hands-on LLM fine-tuning experience, with clear examples of training choices, evaluation results, and production outcomes.
• Strong Python and PyTorch skills.
• Experience building evaluations that informed model selection, releases, or architectural decisions.
• Sound judgment across reliability, security, performance, and cost.
• The ability to set technical direction and bring other engineers along.

Experience with entity resolution, graph ML, model distillation, or domain-specific benchmarks would be useful.