Machine Learning Engineer

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

📋 Description

• Take full ownership of machine learning products from initial problem identification to production and outcome evaluation.

• Engage in discovery, design, construction, deployment, and assessment of results.

• Conduct experiments, analyze outcomes, and determine when to halt ineffective strategies.

• Create canonical datasets and entity-resolution systems for titles, companies, skills, and industries.

• Develop rules-based resolution pipelines that include LLM escalation and durable alias graphs.

• Manage nightly agent loops to adjudicate ambiguous entities and recommend gated structural adjustments.

• Oversee job ingestion at scale, encompassing multi-source feeds, deduplication, freshness, and indexing economics.

• Construct retrieval, ranking, matching, two-tower retrieval, and cross-encoder reranking systems.

• Train models using outcome labels, perform hard-negative mining, implement propensity weighting, and log impression-time data.

• Create mobility embeddings based on observed career trajectories and evaluate realistic career transitions.

• Fine-tune LLMs when cost-effective and develop production-grade agentic systems with human approval checkpoints.

• Continuously infer skills from work artifacts.

• Design product surfaces that require LLMs and discern when LLMs are unnecessary.

• Establish evaluation infrastructure that addresses time-forward splits, calibration, offline-to-online agreement, feedback-loop degeneration, and survivorship bias.

• Ensure compliance with GDPR, EU AI Act high-risk employment-AI requirements, and client data commitments.

⛳️ Requirements

• Over 5 years of experience in deploying ML systems into production.

• Ability to identify the system, metrics before and after implementation, and articulate how the model influenced the changes.

• Expertise in classical ML and deep learning applied to live products, utilizing either PyTorch or TensorFlow.

• Proficient in working with LLMs in a production environment, including retrieval, evaluations, prompt engineering, and context engineering.

• Ability to discern when an LLM is not the appropriate tool.

• Experience in deploying with agentic coding tools like Claude Code, Claude Design, or similar alternatives.

• Capability to present repositories, pull requests, or completed work developed with agentic coding tools.

• Strong foundation in software engineering sufficient to manage deployments.

• Proficient in Python.

• Proficient in Git.

• Experience with cloud environments, specifically AWS.

• Familiarity with containerization.

• Patience for dealing with messy, human-generated, and self-reported data.

🏝️ Benefits

• Fully remote/work-from-home position.

• Equal Employment Opportunity Employer.