Machine Learning Engineer

Career.io • ,

6d • Remote

About The Position

Careerminds is seeking Machine Learning Engineers to join their growing team. This role involves owning products end-to-end, from problem definition to production deployment and metric validation. Engineers will have significant autonomy and accountability, including managing experiments that do not yield desired results. The company emphasizes an AI-native development approach, with engineers utilizing tools like Claude Code and Claude Design as their default. Candidates are expected to demonstrate experience in this way of working through their past projects, repos, or shipped work. This is a 100% remote/work-from-home position.

Requirements

• 5+ years of experience shipping ML systems into production, with the ability to detail the system, its metrics, and causal impact.
• Depth in both classical ML and deep learning (PyTorch or TensorFlow) applied to live products.
• Working fluency with LLMs in production, including retrieval, evals, prompt and context engineering, and the judgment to know when an LLM is the wrong tool.
• Experience shipping with agentic coding tools like Claude Code, Claude Design, or equivalents.
• Strong software engineering fundamentals, including Python, Git, cloud platforms (AWS), containers, and experience with messy, self-reported data.
• Ability to own own deploys.

Nice To Haves

• Entity resolution, record linkage, or taxonomy design at scale
• Ranking, recommendation, or two-tower retrieval systems
• Sequence models on longitudinal or event-stream data
• Embedding and vector retrieval systems in production
• Experiment design, causal inference, or off-policy evaluation
• Warehouse-native ML (dbt, Snowflake, or similar)
• Labor market, HR tech, or people-data domain experience
• Open-source contributions or publications

Responsibilities

• Develop and maintain canonical data and entity resolution systems, including datasets for titles, companies, skills, and industries.
• Implement rules-based resolution pipelines with LLM escalation, focusing on building durable alias graphs.
• Manage nightly agent loops for adjudicating ambiguous entities and proposing structural changes, ensuring data integrity through invariant checks and blast-radius limits.
• Handle job ingestion at scale, addressing multi-source feeds, deduplication, freshness, and underlying indexing economics.
• Build and improve retrieval, ranking, and matching systems, including job matching v2 with two-tower retrieval and cross-encoder reranking.
• Develop mobility embeddings from observed career sequences to understand substitutability between roles.
• Analyze pivot feasibility, identifying realistic career moves, missing intermediate roles, and successful paths taken by peers.
• Apply LLMs and agents, including fine-tuning against outcome labels and implementing agentic systems with human approval gates.
• Develop continuous skills inference from work artifacts.
• Create new product surfaces that genuinely require LLM solutions, while also recognizing when LLMs are not the appropriate tool.
• Build and defend evaluation infrastructure, including time-forward splits, calibration, offline-to-online agreement, and honest handling of feedback-loop degeneration and survivorship bias.
• Incorporate real-world constraints such as GDPR, EU AI Act high-risk classification for employment AI, and client data commitments into system design.

Benefits

• Diversity in thought and cultural background leads to better teams and stronger companies.
• Talented, qualified employees, regardless of race, color, sex/gender (including pregnancy, gender identity, and gender expression), national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under country or local law.
• Equal Employment Opportunity Employer.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed