Applied ML Engineer

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/834ui6

ppbThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied ML Engineer based in Italy. /b /ppAs an Applied ML Engineer, you'll work at the intersection of machine learning research, experimentation, and production engineering.br/You'll turn ideas from research papers into rigorous experiments, measurable evidence, and reliable products.br/The role spans model evaluation, model internals, inference infrastructure, backend systems, and user-facing product experiences.br/You’ll work hands-on with modern ML models, designing evaluations that reveal what methods can and cannot demonstrate.br/You’ll also build production-grade tooling that makes complex experiments repeatable, observable, and accessible to users.br/The environment values technical judgment, ownership, scientific rigor, and the ability to move comfortably across the technology stack.br/It's an opportunity to help transform emerging ML techniques into practical systems that people can trust. /ph3Accountabilities /h3ulliReproduce and evaluate machine learning research methods using open-weight and API-accessible models. /liliDesign evaluation datasets, probes, scoring approaches, baselines, calibration tests, and experiment harnesses. /liliWork directly with model weights, logits, hidden states, activations, model APIs, and inference infrastructure when required. /liliBuild and extend evaluation infrastructure covering experiment runners, judges, persistence, orchestration, reporting, and reproducibility. /liliTurn research workflows into intuitive product experiences, including experiment configuration, execution, traces, comparisons, reports, and review workflows. /liliInvestigate how verification methods behave when models are modified through fine-tuning, merging, quantization, distillation, safety removal, or deliberate evasion. /liliDesign controlled experiments that distinguish meaningful signals from artifacts, confounders, and misleading correlations. /liliProduce clear technical reports that separate measured evidence from interpretation and hypotheses. /liliDeliver production-quality systems with APIs, asynchronous jobs, databases, observability, testing, deployment, and documentation. /liliContribute across research,



experimentation, engineering, and product as priorities evolve. /liliDuring the first six months, reproduce and document at least one published model-provenance or verification method, including its capabilities, assumptions, and limitations. /liliBuild a repeatable model-verification runner with versioned inputs, artifacts, metrics, and reports, and make at least one verification workflow accessible through the product interface. /liliRun controlled experiments across base, fine-tuned, merged, quantized, and known distilled models, improving understanding of when verification methods succeed, fail, and why. /li /ulh3Requirements: /h3ulliStrong Python engineering skills, with hands-on experience using PyTorch and Hugging Face Transformers. /liliSolid understanding of machine learning evaluation, including dataset design, baselines, metrics, calibration, false positives and negatives, statistical uncertainty, and reproducibility. /liliAbility to read ML research papers critically and implement methods from first principles rather than relying entirely on existing packages. /liliProfessional software engineering experience beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, deployment, and documentation. /liliFamiliarity with open-weight models and a practical understanding of how modern LLM inference systems operate. /liliAbility to work across backend and frontend boundaries, with sufficient React/TypeScript knowledge to help make complex experiments and results understandable to users. /liliStrong experimental and analytical judgment, particularly around distinguishing what evidence demonstrates from what it merely suggests. /liliHigh ownership and initiative, with the ability to identify problems, propose solutions, and drive projects forward independently.



/liliComfort working in a fast-moving startup environment where priorities can change quickly and engineers may operate across multiple functions. /liliExperience with model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluation, interpretability, or related areas is a plus. /liliExperience with activation and representation analysis, probing, model hooks, logits, hidden states, or other model-internals techniques is advantageous. /liliFamiliarity with evaluation and inference infrastructure such as DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL/pgvector, or comparable technologies is beneficial. /liliExperience with Next.js, React, TypeScript, data visualization, or experiment dashboards is a plus. /liliExperience running and serving open-weight models on GPUs, including reasoning about latency, throughput, memory, precision, and cost trade-offs, is valuable. /liliExperience designing adversarial evaluations or testing systems against deliberate attempts to evade detection is an advantage. /liliA strong commitment to producing production-quality code, tests, tooling, and documentation that other engineers can confidently operate and extend. /li /ulh3Benefits: /h3ulliOpportunity to work on applied machine learning at the intersection of research, experimentation, engineering, and product. /liliEnd-to-end ownership across model evaluation, model internals, infrastructure, backend systems, and user-facing experiences. /liliExposure to modern open-weight models, LLM inference systems, and emerging ML verification techniques. /liliA role with significant technical autonomy and the opportunity to shape both experiments and production systems. /liliFast-moving startup environment with evolving priorities and cross-functional collaboration. /liliOpportunity to translate cutting-edge research into practical, measurable, and user-accessible products. /liliThe opportunity to build systems and evaluation methodologies designed to produce evidence that users can understand and trust. /liliLocation: Romania. /liliAdditional compensation, flexibility, healthcare, and other benefits may be provided according to the partner company's employment package and local arrangements. /li /ul /p #J-18808-Ljbffr

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/834ui6