Machine Learning Engineer

About Passive

You’re great at what you do — but finding the next step in your career shouldn’t feel like another job. Passive helps you level up without the grind. In seconds, you can uncover better roles, tailor your resume instantly, and apply privately — all without recruiters, job boards, or wasted hours. Think of it as your career power-up: the tool that quietly helps you move faster, feel in control, and land the opportunities you actually deserve.

Want to join the beta?

The Role

As a Machine Learning Engineer at Passive, you will design, build, and scale the intelligence layer that powers our platform — from role-to-candidate matching and ranking to resume tailoring, personalization, and learning loops.

This is not a research-only role. You will productionize models, integrate them deeply into the product, and ensure they improve continuously as data flows through our marketplace flywheel. You’ll work closely with engineering, product, and the founders to turn ML into a durable competitive advantage.

What You’ll Work On

Core ML Systems

• 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).

AI-Powered Resume & Language Systems

• 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.

ML Infrastructure & Productionization

• 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.

Data & Experimentation

• 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.

Our Tech Stack

You’ll help shape this — but today it includes:

ML & AI

• Python
• PyTorch / TensorFlow / scikit-learn
• LLM APIs (OpenAI or equivalent)
• Embeddings, vector search, similarity models
• NLP pipelines (resume parsing, job description understanding)

Data & Infrastructure

• PostgreSQL
• Redis
• Vector databases (e.g., Pinecone, Weaviate, or similar)
• Cloud-native infrastructure (AWS / GCP)
• Docker, CI/CD
• Monitoring & observability tools

Product Integration

• Backend services in Ruby on Rails
• APIs consumed by React frontend
• ATS integrations and marketplace data feeds

What We’re Looking For

Required

• 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.

Strong Plus

• 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.

Why Join Passive

• Own the AI moat of a category-defining platform — not a bolt-on feature. Passive Investment Teaser
• Work on real, high-signal data from both candidates and employers.
• Direct influence on product and company direction — ML is central to Passive’s flywheel.
• Competitive salary meaningful equity.
• Remote-first, async-friendly culture.
• Build AI that actually improves people’s careers — not ad clicks.