Role Description - Assess student abilities and provide instructional assistance for students in need of academic intervention through 1:1 or group workshops as directed by the classroom teacher and designated instructional leader. - Commit to educating the whole student – academically, morally, and socially. - Implement result-driven curriculum to achieve significant growth from fall to spring testing. - Attend professional development and articulation meetings with classroom teachers and parents as required. Qualifications - Must meet state requirements for being a Title 1 paraprofessional. - Previous experience working in a school or with school age children – preferred. - Knowledge of and the ability to assist in teaching reading, writing, and/or mathematics. - Understanding of and capability to navigate technical systems. Benefits - Work comfortably from home. - Access to a best-in-class curriculum with advanced instructional tools and resources. - Diverse global staff and student body create a rich learning environment. - Ensures a safe learning environment. - Personalized development and coaching opportunities. - Convenient meetings during school hours minimize after-hours commitments. - Systems in place to support student well-being and family engagement. - Colleagues you enjoy working with to serve students. Company Description Based in Michigan, PrepNet Virtual Academy opened in 2020 and serves students K-12 online. At PrepNet Virtual, you can connect passion with purpose.

Role Description In the AI Enabler team, our day is usually full of R&D challenges. Have you ever encountered a situation where you need to expand your AI infrastructure so that the applications can automatically pick the right large language models (LLMs) that are both more cost-efficient and better performing? Most of us probably do nowadays, or at least understand the complexity of making such decisions while keeping track of our cloud budget. One of the team's responsibilities is ensuring that whenever a customer makes AI-related decisions regarding their K8s infrastructure, they are implemented automatically without unnecessary costs or hassle. This is just one small piece of a bigger puzzle. To get into a more detailed perspective, ask yourself the following questions: - How often do you use LLMs? - What is the least expensive LLM you can pick for a given prompt without degrading the quality of the response? - How much do your applications cost per 1 million tokens and how can you improve it? - Which API keys have the biggest waste? - How can you improve your frequently running prompt to use fewer tokens? - What is fine-tuning and how to do it efficiently? - What is a transformer? Being a part of this team would involve design and decision-making end-to-end while collaborating with colleagues from other teams. Cast AI, being a technical product, encourages not only coding something as written in the JIRA ticket but also coming up with new features and potential solutions to customers' problems. Given that the team is working on a technical greenfield project, you will have the opportunity to impact it in many ways positively. Here are some of the tools we use daily: - Python - vLLM, SGLang, TensorRT, PyTorch - ClickHouse - PostgreSQL for persistence - GCP Pub/Sub for messaging - gRPC for internal communication - REST for public APIs - Kubernetes, which our product is evolving around - AWS, GCP, and Azure cloud providers, which are currently supported in our platform - GitLab CI with ArgoCD as our GitOps CD engine - Prometheus, Grafana, Loki, and Tempo for observability Qualifications - 5+ years of hands-on experience in Data Science and Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects. - Strong software engineering skills in Python. - Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines. - Expertise in ML inference optimizations, including techniques such as: - Reducing initialization time and memory requirements; - Utilizing reduced precision and weight quantization; - Inference engine tuning (vLLM, SGLang, TensorRT). - Knowledge of network optimization for distributed ML training and inference. - Understanding of distributed training patterns and checkpointing strategies. - You have to be physically in any of the European countries GMT 0 to GMT +3. - Strong English skills. - Strong verbal and written communication skills. - Ability to work independently and collaborate in a group. Requirements - Evaluate and Analyze LLM performance. - Architect and build inference and training pipelines, directly contributing through hands-on design, model training pipeline, and deployment strategies. - Stay up to date with industry trends. Benefits - Competitive salary (€6,500 - €9,000 gross, depending on the level of experience) - Enjoy a flexible, remote-first global environment. - Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. - Equity options. - Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. - Spend 10% of your work time on personal projects or self-improvement. - Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. - Annual hackathon to spark new ideas and strengthen team bonds. - Team-building budget and company events to connect with your colleagues. - Equipment budget to ensure you have everything you need. - Extra days off to help maintain a healthy work-life balance. Hiring process - Screening call with Recruiter - Hiring Manager interview - Technical interview (system design) - Live coding - Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit.

Gestalte mit uns die datengetriebene Zukunft. Du möchtest Unternehmen helfen, bessere Entscheidungen durch intelligente Datenlösungen zu treffen? Als (Senior) Consultant bei Blueforte entwickelst du moderne Analytics-Architekturen und begleitest unsere Kund:innen auf dem Weg zur datengetriebenen Organisation – strategisch, technisch und partnerschaftlich. Deine Perspektiven. Unsere Benefits. New Work-Kultur. Weil Arbeit mehr sein kann: 9 Gründe für Blueforte. - #INNOVATIVE - Vielfalt statt Routine. Du arbeitest an spannenden Projekten, mit wechselnden Herausforderungen und Kund:innen in einer Zukunftsbranche. - #WORKLIFEFLOW - Flexibel, wie es zu dir passt. Arbeite remote in ganz Deutschland oder nutze unsere Coworking-Spaces – abgestimmt auf dein Projekt und dein Leben. - #POWERBREAK - Erholung, die zu dir passt. 30 Urlaubstage, 3 zusätzliche Me-Time-Tage und Workation im EU-Ausland. Oder mal länger raus? Ein Sabbatical macht‘s möglich. - #CAREERBOOST - Dein Weg. Deine Entwicklung. Ob Fachlaufbahn, Projektleitung, Kund:innenmanagement oder Führungsrolle – wir fördern dich mit Trainings, Zertifizierungen und individueller Weiterbildung. Natürlich übernehmen wir die Kosten. - #MINDFLEX - Ideen haben Raum. Deine Ideen zählen: Für Business-Development-Initiativen und Sales-Support-Aktivitäten steht dir ein unternehmensweites Budget zur Verfügung, auf das du dich mit überzeugenden Ideen bewerben kannst. Zusätzlich kannst du unser CloudLab als Innovationsspielwiese nutzen. - #CULTURECONNECT - Hier zählt Teamgeist. Flache Hierarchien, starke Kolleg:innen, eindrucksvolle Events. Bei Blueforte erlebst du Zusammenhalt, der inspiriert und trägt. - #FITGESUND - Weil deine Gesundheit zählt. Mitgliedschaft bei Urban Sports Club oder EGYM Wellpass, Jobrad, betriebliche Krankenzusatzversicherung – und ein durchdachtes Corporate Health Management. - #FAMILYTIME - Wir denken Familie mit. 10 Tage Kind krank pro Jahr – ohne Bürokratie, mit vollem Gehalt. Sowie Kitazuschuss für deine Betreuungskosten. - #RESPONSIBLE - Nachhaltig unterwegs. E-Mobility, Bahnfahrten 1. Klasse und ÖPNV-Zuschuss – für deine smarte Mobilität. Was du bei uns machen wirst. - Als KI-Expert:in entwickelst und optimierst du Machine Learning- und Deep Learning-Modelle für verschiedene Anwendungsfälle und setzt sie produktiv ein. - Du analysierst komplexe Datenstrukturen, leitest passende statistische und mathematische Verfahren ab und bringst dein Wissen in innovative KI-Lösungen ein. - Du arbeitest mit gängigen ML-Frameworks wie scikit-learn, PyTorch oder TensorFlow und nutzt Tools wie Jupyter Notebooks, Huggingface oder Llama Index für deine Projekte. - Du entwickelst gut strukturierten, wartbaren Code (bevorzugt in Python) und orientierst dich dabei an Best Practices. - Du bringst dich aktiv in den gesamten Entwicklungszyklus ein – von der Konzeption über die Modellvalidierung bis hin zur Implementierung und Wartung der KI-Lösungen. Wir suchen dich. - Du hast ein abgeschlossenes Studium in einem naturwissenschaftlichen Fach und fundiertes Wissen in Statistik und Mathematik. - Du verfügst über tiefgehende Erfahrung mit Machine Learning- und Deep Learning-Techniken und hast diese bereits produktiv eingesetzt. - Du bist sicher in der Programmierung, idealerweise mit Python, und kennst dich mit Code-Versionierung (z. B. Git) aus. - Erste Berührungspunkte mit Data Engineering und Requirements Engineering runden dein Profil ab. - Du bist kommunikativ, lösungsorientiert und ein:e echte:r Hands-on-Teamplayer:in, der:die Zusammenarbeit auf Augenhöhe schätzt und Teamspirit lebt. - Du sprichst fließend deutsch und verfügst über gute Englischkenntnisse. - Du arbeitest überwiegend remote und solltest daher bereit sein, regelmäßig zu Kund:innen in ganz Deutschland zu reisen.

• Design, build, and validate machine learning models for RF emitter identification — including feature engineering from sensor data, training pipeline development, model evaluation, and iterative refinement based on results • Conduct hands-on exploratory data analysis on RF sensor datasets using Python and Jupyter notebooks — writing and running analytical code, characterizing feature distributions, identifying data quality issues, and producing documented findings • Implement and maintain ML data pipelines — ingesting NDF sensor streams, applying rollup and preprocessing logic, constructing training datasets, and ensuring pipeline correctness on constrained edge hardware with no cloud dependency • Collaborate with the technical lead and Principal AI/ML Engineer to investigate RF sensor data quality, attribution reliability, and feature behavior under contention — writing code to characterize error sources, validate assumptions, and reproduce findings • Produce clear technical documentation of experiments, model configurations, and results — maintaining reproducibility through disciplined versioning, and contributing to monthly status reports and team knowledge sharing