Machine Learning Engineer, AI Safety & Assurance - USDS

endation and General Purpose AI (GPAI) systems. You will build the high-performance engines that audit, evaluate, and mitigate risks in real-time.

Core Focus Areas:

• Recommendation & GPAI Assurance: Architecting auditable recommendation pipelines to ensure content delivery systems meet strict USDS compliance and safety requirements.
• Algorithmic Interpretability: Enhancing the transparency of recommendation signals to ensure model decisions are explainable and aligned with safety protocols.
• Continuous Monitoring & Mitigation: Developing automated pipelines to evaluate, monitor, and fine-tune GPAI/LLM deployments to proactively mitigate risks such as bias, toxicity, and misinformation.

Responsibilities

• Automated Safety Evaluation: Design and scale high-throughput evaluation engines to benchmark LLM and Recommendation Model outputs against safety, bias, and accuracy protocols.
• Adversarial Robustness: Build and maintain continuous monitoring frameworks to proactively identify vulnerabilities in recommendation logic and model behavior.
• Governance-as-Code: Architect metadata frameworks and control planes for ML pipelines, ensuring training data and model weights adhere to purpose-limitation and safety constraints.
• Real-time Mitigation: Develop low-latency safety filters and "circuit breakers" that can intervene in real-time recommendation streams (ms-level latency) without compromising system performance.
• Observability & Lineage: Create centralized platforms that provide real-time observability into model decisions, ensuring every algorithmic "promotion" is traceable, auditable, and compliant.

Qualifications

Minimum Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field.
• Software Engineering: 2+ years of experience in production-level software development (Go, Python, or C++).
• Systems at Scale: Proven experience with Distributed Systems and handling high-volume ML data pipelines (e.g., Spark, Flink, or Kafka).
• ML Foundations: Strong grasp of Data Structures, Algorithms, and Machine Learning fundamentals (Deep Learning, Optimization, Evaluation Metrics).

Preferred Qualifications:

• AI Safety Expertise: Direct experience in AI Alignment, Model Interpretability (XAI), or Adversarial Machine Learning.
• LLM Specialization: Familiarity with LLM evaluation frameworks (e.g., G-Eval, RAGAS), safety fine-tuning (RLHF/DPO), or prompt-injection defense.
• RecSys Knowledge: Deep understanding of modern content recommendation systems (e.g., Two-tower models, Deep Ranking, Reinforcement Learning).
• Full-Stack ML Tech: Proficiency in Go (Golang) for high-performance backend infrastructure and Python/PyTorch for ML modeling and research.
• Regulatory Translation: A proven ability to translate complex legal and safety policies into rigorous, scalable technical implementations.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $136800 - $359720 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.