Software Engineer - Machine Learning

FocusKPI Inc. • Mountain View, CA

1d • $95 - $110 • Onsite

About The Position

FocusKPI is seeking a Software Engineer - Machine Learning to join one of our clients, a high-tech SaaS company. We are looking for an experienced Machine Learning Engineer to lead the development of prompt-injection and prompt-safety models to protect the client's downstream agentic AI systems across phones, the cloud, and XR/AR. You 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 using RLHF, DPO, and related optimization techniques to push detection accuracy and false-positive rates beyond what off-the-shelf solutions can achieve.

Requirements

• 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 post-master's degree graduation.
• 2+ years of industry experience in software engineering post-master's degree graduation.
• 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).

Nice To Haves

• 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 (e.g., GCG, PAIR, generator–critic frameworks).
• Experience with on-device or edge ML deployment (ExecuTorch, Core ML, TFLite, MLC-LLM, vendor NPU toolchains) and model compression (quantization, distillation, pruning) for safety models.
• Experience with telemetry, logging, or user-facing data systems on mobile, XR/AR, or consumer platforms, including privacy-preserving handling of user data (e.g., anonymization, on-device processing, federated approaches).
• Publications at top-tier ML/NLP/security venues (NeurIPS, ICML, ICLR, ACL, EMNLP, USENIX Security, IEEE S&P), patents, or open-source contributions in the safety, alignment, or AI security space.

Responsibilities

• Design and train prompt-injection detection models and prompt-safety classifiers that operate on both inputs to and outputs from the client's 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior