Machine Learning Engineer

Passive is a company that helps individuals level up their careers without the traditional job search grind. As a Machine Learning Engineer, you will design and build the intelligence layer for their platform, focusing on candidate-to-role matching, ranking systems, and integrating machine learning models into the product.

Responsibilities:

• Design and improve candidate-to-role matching models using structured and unstructured data
• Build intelligent ranking, scoring, and recommendation systems that improve with scale
• Develop personalization models that adapt to user behavior, preferences, and career trajectories
• Own evaluation frameworks to measure relevance, precision, and downstream outcomes (applies, interviews, hires)
• Build and iterate on resume parsing, normalization, and enrichment pipelines
• Integrate LLMs for AI resume tailoring, skill extraction, and ATS optimization
• Combine classical ML with LLM-based systems for reliability, cost control, and explainability
• Develop guardrails and quality checks to ensure trust, accuracy, and consistency
• Deploy models into production with monitoring, versioning, and rollback strategies
• Build scalable inference pipelines that integrate with backend services (Ruby / APIs)
• Implement feedback loops that retrain and improve models over time
• Partner with engineering on data pipelines, feature stores, and model observability
• Work with real-world behavioral data from both candidates and employers
• Design A/B tests to validate ML impact on engagement, conversion, and match quality
• Help define data schemas and instrumentation to support long-term ML velocity

Requirements:

• 4+ years of experience in machine learning or applied AI roles
• Strong foundation in ML concepts (ranking, classification, NLP, recommendation systems)
• Experience deploying ML models into production environments
• Proficiency in Python and modern ML frameworks
• Ability to reason about tradeoffs between model quality, latency, cost, and complexity
• Product mindset — you care about real-world impact, not just metrics
• Experience working with LLMs, embeddings, or hybrid ML + LLM systems
• Background in search, matching, recommendations, or personalization
• Experience with marketplace, two-sided platforms, or HR / recruiting data
• Startup or early-stage experience (Seed–Series A)
• Familiarity with ATS systems or resume/job data