Machine Learning Engineer
Career.io (TalentWW) · Remote
About this role
## Machine Learning Engineer (Remote, Full-Time) ### About the Company Careerminds is a leader in career transition and coaching solutions, helping organizations support employees through change while enabling workforce growth and development. Our product portfolio includes Career Transition and Coaching Services, plus Progression—our application for Career Frameworks and progression planning. ### The Role We’re growing our machine learning team and looking for **Machine Learning Engineers** who **own products end to end**—from the problem, to production, to the metric that proves it worked. You’ll work with autonomy similar to a founder within your domain, with accountability that includes the unglamorous parts of ML: running experiments that may not pan out, and making the call to stop them. We value shipping what works over shipping what only looks good on dashboards. **AI-native development is the baseline.** Engineers ship with **Claude Code** and **Claude Design** (or close equivalents), and the leverage from this is why one engineer can own a product end to end. In interviews, we’ll ask you to show the trail: **repos, PRs, or shipped work** demonstrating this approach. **This is a 100% remote / work-from-home role.** ### Key Responsibilities (Areas of Focus) Depending on your focus, you may work on: - **Canonical data & entity resolution** - Canonical datasets for titles, companies, skills, and industries - Content-addressed IDs, faceted taxonomies, alias graphs across tens of millions of rows - Rules-based resolution pipelines with LLM escalation - Nightly agent loops to adjudicate ambiguous entities with invariant checks and blast-radius limits - **Job ingestion at scale** - Multi-source feeds, deduplication, freshness - Indexing economics - **Retrieval, ranking, and matching** - Job matching v2: two-tower retrieval + cross-encoder reranking - Training on outcome labels (not clicks) - Hard-negative mining, propensity weighting, impression-time logging - **Mobility & sequence-based embeddings** - Mobility embeddings learned from observed career sequences - Capturing substitutability even when vocabulary differs - **Pivot feasibility** - Given where someone is, determine realistic moves, missing pieces, and intermediate roles that worked for peers - **Applied LLMs & agents** - Fine-tuning where it earns its cost (against outcome labels) - Agentic systems in production with **human approval gates** - Continuous skills inference from work artifacts (not static documents) - New product surfaces where the right answer genuinely requires an LLM - **Evaluation & experimentation infrastructure** - Defend evaluation approaches in design reviews: time-forward splits, calibration, offline-to-online agreement - Handle feedback-loop degeneration and survivorship bias honestly - **Building within real constraints** - GDPR, EU AI Act high-risk classification for employment AI, and client data commitments ### The Must-Haves - **5+ years** shipping ML systems into production - You can name the system, the metric before/after, and how you knew the model caused the change - Depth in **classical ML and deep learning** (PyTorch or TensorFlow) applied to live products - Working fluency with **LLMs in production** - Retrieval, evals, prompt/context engineering, and judgment to know when an LLM is the wrong tool - Experience shipping with **agentic coding tools** (Claude Code / Claude Design or c
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