Machine Learning Engineer

Job not on LinkedIn

🕒 August 21

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 22%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Talent Inc.

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

Talent Inc. is a career development company focused on helping individuals enhance their job-seeking process and career progression. They provide a range of services including professional resume building, cover letter creation, and career coaching to assist clients in landing their desired positions. Their platform offers tools such as AI career assessments, job application automation, interview preparation, and salary negotiation assistance to support users at various stages of their career, from students and early career professionals to executives looking to make a significant career change.

📋 Description

• Own machine learning products end to end, from problem definition through production and outcome metrics • Conduct discovery, design, building, shipping, and measurement of results • Run experiments, evaluate outcomes, and decide when to discontinue unsuccessful approaches • Build canonical datasets and entity-resolution systems for titles, companies, skills, and industries • Develop rules-based resolution pipelines with LLM escalation and durable alias graphs • Operate nightly agent loops that adjudicate ambiguous entities and propose gated structural changes • Handle job ingestion at scale, including multi-source feeds, deduplication, freshness, and indexing economics • Build retrieval, ranking, matching, two-tower retrieval, and cross-encoder reranking systems • Train models on outcome labels, perform hard-negative mining, propensity weighting, and impression-time logging • Develop mobility embeddings from observed career sequences and assess realistic career moves • Fine-tune LLMs when cost-effective and build production agentic systems with human approval gates • Infer skills continuously from work artifacts • Create product surfaces requiring LLMs and identify when LLMs are unnecessary • Build evaluation infrastructure covering time-forward splits, calibration, offline-to-online agreement, feedback-loop degeneration, and survivorship bias • Design within GDPR, EU AI Act high-risk employment-AI requirements, and client data commitments

🎯 Requirements

• 5+ years shipping ML systems into production • Ability to name the system, metric before and after, and explain how the model caused the change • Depth in classical ML and deep learning applied to live products, using PyTorch or TensorFlow • Working fluency with LLMs in production, including retrieval, evaluations, prompt engineering, and context engineering • Judgment to recognize when an LLM is the wrong tool • Experience shipping with agentic coding tools such as Claude Code, Claude Design, or close equivalents • Ability to show repos, PRs, or shipped work built with agentic coding tools • Strong software engineering fundamentals sufficient to own deployments • Python proficiency • Git proficiency • Cloud experience, specifically AWS • Experience with containers • Patience for messy, human-authored, self-reported data

🏖️ Benefits

• 100% remote/work-from-home role • Equal Employment Opportunity Employer

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