ML Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Design, train, assess, and deploy machine learning systems that enhance governance and security functionalities, beginning with challenges such as prompt injection detection, behavioral anomaly detection, trust scoring, and policy suggestions.

• Develop the essential infrastructure: data pipelines, feature stores, model serving, evaluation frameworks, and feedback loops that facilitate rapid iteration.

• Make informed decisions regarding build-vs-buy. Leverage frontier models, ready-made tools, and managed services to accelerate progress; invest in custom systems where they provide a lasting competitive edge.

• Set the technical direction for the team’s machine learning initiatives. Take responsibility for the architecture, evaluation methods, model lifecycle, and quality standards for deployment.

• Assist in recruiting, mentoring, and shaping the team as it expands.

• This position may involve participating in a 24/7 on-call rotation for the Agentic Platform; assume genuine pager responsibility for the services you design and manage.

⛳️ Requirements

• Over 5 years of in-depth applied machine learning and AI experience with a proven history of delivering production systems. Experience in fraud, abuse, safety, security, or trust sectors, where adversarial dynamics, imbalanced data, and critical decision-making are significant.

• At least 4 years of professional, hands-on, full-time software engineering experience in backend, infrastructure, or platform engineering.

• A Bachelor’s degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.

• Demonstrated ability to build and manage systems surrounding machine learning models, including data pipelines, serving, evaluation, and monitoring, while successfully delivering customer-facing products from start to finish.

• Proficient in utilizing modern AI tools in your daily work, with a keen instinct for recognizing when frontier models can substitute traditional ML, when they cannot, and when to integrate both approaches.

• Experience with LLM-based systems in a production environment - including evaluation, prompt engineering, fine-tuning, retrieval, guardrails, and agent frameworks.

• Knowledge of the agent/MCP ecosystem.

• You thrive in an early-stage environment where the roadmap is being developed concurrently with the work, making clear decisions with limited information.

• Collaborative and low-ego; you excel in cross-team interactions, communicate clearly, and engage others effectively.

🏝️ Benefits

• Freedom & flexibility; tailor your work around your personal life.

• Designated quarterly Whaleness Days along with an end-of-year Whaleness break.

• Home office setup; we aim to ensure your comfort while working.

• 16 weeks of paid parental leave (after 6 months of employment).

• Technology stipend equivalent to $100 USD net/month.

• PTO plan that encourages you to take time for activities you enjoy.

• Training stipend for conferences, courses, and classes.

• Equity; as a growing start-up, we want all employees to share in the company’s success.

• Docker swag.

• Medical benefits, retirement plans, and holidays may vary by country.

• Remote-first culture, with offices located in Seattle and Paris.