Senior Machine Learning Engineer

Rise Technical; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Rise Technical is seeking a Senior ML Engineer to own autonomous, multi-agent architectures within a high-growth cybersecurity AI startup. You will design, implement, and optimize production-grade LLM systems, and work closely with leadership to advance the flagship platform.

You will contribute to model fine-tuning and data curation, and help deploy scalable agent systems in Kubernetes, with a focus on latency and throughput optimizations for real-time security applications.

Qualifications

• Experience building production ML systems with LLMs.
• Strong Python, PyTorch, and Hugging Face.
• Experience with Supervised Fine-Tuning, preference optimization (DPO/GRPO).
• Experience shipping multi-agent or tool-using LLM systems to production.

Responsibilities

• Design and improve multi-agent architectures, planning loops, and tool-use
• Contribute to model training via data curation, Supervised Fine‑Tuning, and preference optimization (DPO/GRPO)
• Build evaluation harnesses to track model and agent performance
• Optimize inference for latency and throughput using vLLM or TensorRT-LLM
• Deploy and scale production agents within Kubernetes environments

Skills

LLM agents

Agent orchestration

Model fine-tuning

Latency optimization

Tools

Python

PyTorch

Hugging Face

Kubernetes

vLLM

TensorRT-LLM

Job description

$150,000 -$230,000 + Equity + PTO

Are you a Senior ML Engineer with experience building production-grade LLM systems looking to take ownership of autonomous, multi-agent architectures within a high-growth cybersecurity AI start-up?

This is an opportunity to join a fast-growing AI company developing next-generation autonomous offensive security platforms. You will join a specialized AI engineering team and play a critical role in advancing their flagship platform, moving the needle on complex problems like exploit chaining, multimodal vision, and co‑evolutionary self‑training loops.

In this role, you will design, implement, and optimize highly sophisticated multi-agent architecture, while contributing directly to model fine‑tuning and production scalability. Working closely with engineering leadership and cross-functional teams, you will transition advanced multi-agent systems from cutting‑edge experimentation into robust, production‑ready enterprise solutions.

This role would suit an ML Engineer who enjoys solving deep, technical agent‑orchestration challenges and wants to be a foundational builder of autonomous, self‑improving security systems.

The Role:

• Design and improve multi-agent architectures, planning loops, and tool-use
• Contribute to model training via data curation, Supervised Fine‑Tuning, and preference optimization (DPO/GRPO)
• Build evaluation harnesses to track model and agent performance
• Optimize inference for latency and throughput using vLLM or TensorRT-LLM
• Deploy and scale production agents within Kubernetes environments

The Person:

• Experience building production ML systems, with a strong focus on LLMs or LLM-powered agents
• Strong Python programming alongside PyTorch and Hugging Face
• Experience with Supervised Fine‑Tuning, preference optimization, and synthetic data generation
• Experience shipping multi‑agent or tool‑using LLM systems to production