Machine Learning Engineer

• Seeking an experienced Machine Learning Engineer to lead the development of prompt injection and prompt safety models that protect downstream agentic AI systems across phone, cloud, and XR/AR.
• Role will design, train, and deploy classifier and guardrail models (both cloud-based and hybrid on-device) that screen agent inputs and outputs for injection attacks, unsafe content, and policy violations. A core part of the role is post-training these models with RLHF, DPO, and related optimization techniques to push detection accuracy and false-positive rates beyond what off-the-shelf solutions provide.

• Design and train prompt injection detection models and prompt safety classifiers that operate on both inputs to and outputs from agentic AI systems.
• Build hybrid deployment pipelines that split safety inference between on-device (phone, XR/AR) and cloud, optimizing for latency, privacy, and detection coverage.
• Apply post-training techniques (e.g. RLHF, reward modeling, policy optimization) to optimize guardrail model performance, calibration, and robustness against adaptive adversaries.
• Curate and generate adversarial training data: direct and indirect prompt injections, jailbreaks, tool-use exploits, and unsafe-output cases drawn from red-teaming and production signals.
• Build evaluation harnesses that measure attack success rate false-positive rate latency, and on-device footprint across model iterations and threat categories.
• Partner with agent, device, and platform teams to integrate safety models into mobile-use agents, XR/AR assistants, and cloud agentic workflows, and to close the loop from production incidents back into training data.
• Work cross-functionally with security researchers, modeling teams, and product engineers; document methods and, where appropriate, contribute to patents and publications.

• M.S. or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field; or B.S. with equivalent industry experience.
• 3+ years of industry experience in ML engineering or applied AI research, with demonstrated ownership of production ML systems.
• 2+ years of industry experience in software engineering.
• Strong proficiency in Python and PyTorch (or JAX/TensorFlow), with solid software engineering fundamentals (version control, testing, and reproducible experimentation).
• Hands-on experience post-training LLMs with RLHF, DPO, RLAIF, or reward modeling including reward design, preference data curation, and training stability.
• Hands-on experience training and deploying classifier or guardrail models for safety, content moderation, abuse detection, or adversarial robustness.
• Familiarity with prompt injection, jailbreak, and agentic AI threat models, and with distributed training frameworks (DeepSpeed, FSDP, Accelerate).

• Experience building safety or moderation systems for agentic AI: tool-use guardrails, indirect prompt injection defenses, or output filtering for autonomous agents.
• Experience with red-teaming, adversarial data generation, or automated attack pipelines.

Who We Are:
The Fountain Group is a nationwide staffing firm with over 80 Fortune 100-500 clients. Since 2001, TFG has maintained a consistent standard of excellence, and our work is broadly recognized every year through numerous industry performance awards. Our success is a team effort.
Browse our website below for additional information on our company.
The Fountain Group
3407 W Martin Luther King Jr. Dr. Tampa, FL 33607
“We work in Life Sciences, Clinical, Engineering, IT, and more. Above all, we specialize in people.”

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