Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4361996665&distance=25.0&f_TPR=r86400&f_WT=2&geoId=103644278&keywords=software%20engineer&origin=JOB_SEARCH_PAGE_JOB_FILTER&spellCorrectionEnabled=true&start=175 Job Description: Senior Machine Learning Engineer Boston, MA · 2 hours ago · 14 applicants Promoted by hirer · Company review time is typically 1 week Remote Matches your job preferences, workplace type is Remote. Full-time Matches your job preferences, job type is Full-time. Easy Apply Save Save Senior Machine Learning Engineer at Passive Senior Machine Learning Engineer Passive · Boston, MA (Remote) Easy Apply Save Save Senior Machine Learning Engineer at Passive Show more options Your profile is missing required qualifications Show match details Help me update my profile BETA Is this information helpful? Get personalized tips to stand out to hirers Find jobs where you’re a top applicant and tailor your resume with the help of AI. Try Premium for PKR0 About the job About Passive We all know someone stuck in a job they’ve outgrown. Not because they’re unmotivated — but because finding something better takes time they don’t have. Passive exists to fix that. We’re building the world’s first career discovery app for busy professionals who are open to better opportunities but don’t want to spend hours on job boards. Every feature we build reduces friction, saves time, and gives people back control of their careers. Instead of searching job boards, users discover curated roles, instantly tailor resumes, and apply in seconds — all in a private, mobile-first experience. Think Instagram meets LinkedIn, backed by serious AI infrastructure. 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. 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.