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Senior ML Engineer | Kimchi (Inference Stack Optimization)
CAST AI

Remote Italy

" Why Kimchi? Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters today we run our own coding harness on any model you pick and we run the models underneath it ourselves. Kimchi Inference One API line and a customer's existing client just works DeepSeek GLM Kimi MiniMax Nemotron served serverless from our own GPU fleet (OpenAI- and Anthropic-compatible pay per token spend visible per user team and key). When control matters the same models run on the customer's own cluster instead. Behind that one line sits the machinery you'll own SGLang and vLLM engines B300 nodes a shared cluster filesystem that holds every checkpoint exactly once KV caches riding coding traffic with heavy prefix reuse and the benchmark harness that gates every change we ship. Made by Cast AI — a Miami-headquartered globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. About the role Throughput. Latency. Cold starts. Cost per token. Move those four numbers in the right direction and two things happen customers get faster cheaper inference and our margins improve. That's the entire thesis of this role. Every loader you tune every scheduler decision you land every kernel you stare at shows up in a customer's p99 and on our P&L. Your mandate is the inference stack's optimization whatever it takes and the technical direction of how we optimize it is yours to argue with data. The problem the checkpoints run 0.3-1.5 TB and the ""right"" serving configuration depends on traffic shape sequence-length distribution batch dynamics GPU SKU memory bandwidth and quantization tolerance all variables that shift week to week. Most teams pick a configuration once over-provision GPUs and absorb the cost. We don't we took Kimi-K3 (1.5 TB) from over an hour cold-to-serving to under twelve minutes with a shared filesystem ODIRECT weight loaders and node-local JIT caches and we're nowhere near done. Prefill/decode disaggregation and tiered KV caches are next. Stack Python SGLang vLLM TensorRT-LLM PyTorch fastsafetensors CUDA-adjacent tooling Kubernetes virtiofs shared filesystems ClickHouse OpenTelemetry Prometheus Grafana GitLab CI. Requirements You've built real ML systems for 5+ years and your portfolio shows depth in inference or training infrastructure not just model training notebooks. Your Python is production-grade services not scripts. You've run vLLM SGLang or TensorRT-LLM for real and you have a working mental model of why an engine performs the way it does on a given GPU. You've measured quantization regressions not just compression ratios. Multi-GPU and multi-node setups hold no fear tensor and expert parallelism collective communication and their practical failure modes. You know where the bytes live (page caches ODIRECT network filesystems under terabyte-scale reads) and how fast they move. You instrument before you optimize and you can tell a real win from a benchmark artifact. A wide mandate excites you more than it unsettles you. Nice to haves You've worked on LLM routing specifically RouterBench-class evals cost/quality/latency Pareto fronts. Calibration abstention or conformal methods in production decision systems. You've run online A/B loops with drift monitoring on a model that touches real money. Responsibilities Push throughput. Continuous batching chunked prefill speculative decoding with draft models CUDA-graph capture and scheduler tuning across the engine fleet. Find the ceiling on each GPU SKU then raise it. Cut latency. Attack TTFT and inter-token latency separately profile to find the actual bottleneck (compute memory bandwidth scheduling storage or network) and fix it not the one you assumed. Get more out of the KV cache. Hierarchical and paged attention prefix caching on heavy-reuse coding traffic eviction policies quantized KV. A lot of the unrealized throughput lives here and you'll own it. Kill cold starts. Weight loaders (ODIRECT fastsafetensors-class) JIT and compile caches shared-filesystem read paths. A 1.5 TB checkpoint has no business taking an hour to reach serving. Quantize without regressing quality. INT8/INT4 FP8/FP4 across weights activations and KV measured on real workloads not just perplexity benchmarks. Scale across nodes. Distributed inference topologies network-aware placement prefill/decode disaggregation checkpointing strategies that don't bottleneck on storage or interconnect. Set the technical direction. Decide what we benchmark what we adopt and what we build ourselves. Bring the team along with strong writeups and reproducible experiments. What's in it for you? Competitive salary (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. LI-Remote"

New 8 min(s). ago 🔥 View
Senior Applied ML Engineer | Kimchi (Model Router)
Kimchi

Remote Italy

Why Kimchi? Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters today we run our own coding harness on any model you pick and the router deciding which model answers each request is ours too. The Auto Router Our promise is one enforcement layer for your AI budgets spend attribution and model routing applied before a request ever hits a model. The Auto Router makes the call for every request which model serves it. Eight open-source models whose input prices span more than 20× cached input up to 50× cheaper than fresh frontier providers on customers' own keys and a coding agent that fires hundreds of requests before its task is done. Frontier-level task success at a fraction of the token cost that sentence is the product and it stays true only if the model inside the router keeps getting better. That model is yours. Made by Cast AI — a Miami-headquartered globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. About the role Every request gets a decision which model serves it. This is an applied-ML seat not an infrastructure one. Platform engineers own the proxy the streaming path and the fleet. You own the model inside them the training data the objective the evaluation and the iteration loop that ships a better router every cycle. The problem routing is a prediction problem wearing a product's clothes. For every request you predict the quality each candidate model would deliver what it would cost and how fast it would decode and you have to be right enough in aggregate that the savings are real and the quality never dips below frontier. The data is messy rollout outcomes from benchmark harnesses per-request records from the live fleet override signals and token streams whose speed depends on the serving concurrency at the moment. Speed itself is a correctness variable on deadline-bound agentic work a model that decodes three times slower doesn't do the same work slower it does less work. That's why naive cost-per-token objectives quietly optimize for the wrong thing. And the bar for evidence is high a router change ships only after production-parity evaluation with error bars then gets held to its numbers in live traffic. Stack Python PyTorch SQL at scale (ClickHouse) OpenAI- and Anthropic-compatible LLM APIs benchmark and rollout harnesses (RouterBench-class) GitLab CI. Requirements You've spent 5+ years in applied ML or ML research and you've trained evaluated and shipped models that make real-time decisions (ranking recommendation bandits pricing or LLM routing) and you can tell that story with receipts. Strong Python and SQL at scale you can pull clean and interrogate millions of request-level records without waiting on anyone. You know the difference between a model that got better and an eval that got easier. You have real opinions about what LLM benchmarks measure agentic harnesses included. You know enough serving reality (TTFT decode speed batching prefix caching concurrency) to model the objective honestly. You instrument before you optimize error bars and all. A wide mandate excites you more than it unsettles you. Nice to haves You've worked on LLM routing specifically RouterBench-class evals cost/quality/latency Pareto fronts. Calibration abstention or conformal methods in production decision systems. You've run online A/B loops with drift monitoring on a model that touches real money. Responsibilities Own the routing model end to end features objective training calibration and release cadence. Iterate it like a product not a research artifact. Build the evaluation that gates every change production-parity benchmarks router-vs-router comparisons holdouts by task family error bars on pass rates. Nothing ships on vibes. Turn fleet data into training signal per-request outcomes from production benchmark rollouts and override feedback with failure categorization (infra vs task) so poisoned labels never reach the policy. Model the objective the user actually experiences task success × token cost × wall-clock speed under the real concurrency regime with cached-input economics in the picture. Run the online loop shadow A/B full rollout drift monitoring retrain. When production disagrees with your offline eval you find out why before the next ship. Push the frontier where it pays calibrated decisions abstention under uncertainty speed-aware routing. We experiment on what a router should be not just which model it picks today. Partner with platform engineers on instrumentation the features and labels recorded per request (decode speed first-class among them) and the experiments the data path must support. What's in it for you? Competitive salary (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. LI-Remote

New 8 min(s). ago 🔥 View
Senior Applied ML Engineer | Kimchi (Model Router)
CAST AI

Remote Italy

Why Kimchi? Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters today we run our own coding harness on any model you pick and the router deciding which model answers each request is ours too. The Auto Router Our promise is one enforcement layer for your AI budgets spend attribution and model routing applied before a request ever hits a model. The Auto Router makes the call for every request which model serves it. Eight open-source models whose input prices span more than 20× cached input up to 50× cheaper than fresh frontier providers on customers' own keys and a coding agent that fires hundreds of requests before its task is done. Frontier-level task success at a fraction of the token cost that sentence is the product and it stays true only if the model inside the router keeps getting better. That model is yours. Made by Cast AI — a Miami-headquartered globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. About the role Every request gets a decision which model serves it. This is an applied-ML seat not an infrastructure one. Platform engineers own the proxy the streaming path and the fleet. You own the model inside them the training data the objective the evaluation and the iteration loop that ships a better router every cycle. The problem routing is a prediction problem wearing a product's clothes. For every request you predict the quality each candidate model would deliver what it would cost and how fast it would decode and you have to be right enough in aggregate that the savings are real and the quality never dips below frontier. The data is messy rollout outcomes from benchmark harnesses per-request records from the live fleet override signals and token streams whose speed depends on the serving concurrency at the moment. Speed itself is a correctness variable on deadline-bound agentic work a model that decodes three times slower doesn't do the same work slower it does less work. That's why naive cost-per-token objectives quietly optimize for the wrong thing. And the bar for evidence is high a router change ships only after production-parity evaluation with error bars then gets held to its numbers in live traffic. Stack Python PyTorch SQL at scale (ClickHouse) OpenAI- and Anthropic-compatible LLM APIs benchmark and rollout harnesses (RouterBench-class) GitLab CI. Requirements You've spent 5+ years in applied ML or ML research and you've trained evaluated and shipped models that make real-time decisions (ranking recommendation bandits pricing or LLM routing) and you can tell that story with receipts. Strong Python and SQL at scale you can pull clean and interrogate millions of request-level records without waiting on anyone. You know the difference between a model that got better and an eval that got easier. You have real opinions about what LLM benchmarks measure agentic harnesses included. You know enough serving reality (TTFT decode speed batching prefix caching concurrency) to model the objective honestly. You instrument before you optimize error bars and all. A wide mandate excites you more than it unsettles you. Nice to haves You've worked on LLM routing specifically RouterBench-class evals cost/quality/latency Pareto fronts. Calibration abstention or conformal methods in production decision systems. You've run online A/B loops with drift monitoring on a model that touches real money. Responsibilities Own the routing model end to end features objective training calibration and release cadence. Iterate it like a product not a research artifact. Build the evaluation that gates every change production-parity benchmarks router-vs-router comparisons holdouts by task family error bars on pass rates. Nothing ships on vibes. Turn fleet data into training signal per-request outcomes from production benchmark rollouts and override feedback with failure categorization (infra vs task) so poisoned labels never reach the policy. Model the objective the user actually experiences task success × token cost × wall-clock speed under the real concurrency regime with cached-input economics in the picture. Run the online loop shadow A/B full rollout drift monitoring retrain. When production disagrees with your offline eval you find out why before the next ship. Push the frontier where it pays calibrated decisions abstention under uncertainty speed-aware routing. We experiment on what a router should be not just which model it picks today. Partner with platform engineers on instrumentation the features and labels recorded per request (decode speed first-class among them) and the experiments the data path must support. What's in it for you? Competitive salary (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. LI-Remote

New 8 min(s). ago 🔥 View
Docente GSE
Studio Alfano

Remote Italy

Descrizione dell'azienda Studio Alfano è una realtà professionale che opera nel settore della consulenza e della formazione con particolare attenzione ai temi tecnici e normativi legati alla sicurezza sul lavoro. L’azienda collabora con imprese e professionisti offrendo percorsi formativi strutturati e aggiornati rispetto alle più recenti disposizioni del GSE e delle autorità competenti. L’ambiente di lavoro valorizza autonomia responsabilità e aggiornamento continuo favorendo la crescita delle competenze delle persone collaboratrici. Studio Alfano promuove un approccio inclusivo e orientato alla qualità del servizio con forte attenzione alle esigenze reali di chi partecipa alla formazione. Descrizione del ruolo La persona che ricoprirà il ruolo di Docente GSE sarà responsabile della progettazione preparazione e erogazione di corsi di formazione online sui meccanismi e sulle procedure del GSE. Nel lavoro quotidiano il docente gse dovrà predisporre materiali didattici chiari e aggiornati svolgere sessioni sincrone e asincrone rispondere ai quesiti delle persone partecipanti e monitorare il livello di apprendimento. La posizione è full time e prevede modalità completamente da remoto con utilizzo di piattaforme di videoconferenza e strumenti digitali per la gestione dei contenuti formativi. La persona selezionata collaborerà con il team interno per aggiornare i programmi verificare la coerenza normativa e proporre miglioramenti ai percorsi formativi. È richiesta capacità di organizzare il proprio tempo e di gestire in autonomia il calendario dei corsi e le attività di follow-up. Qualifiche Il/la candidato/a dovrebbe possedere solide competenze tecniche in normativa GSE e regolamentazione del settore energia con esperienza nell’applicazione pratica delle principali procedure. È richiesta capacità di progettazione didattica e gestione della formazione online inclusa la creazione di materiali formativi digitali e l’utilizzo di piattaforme e-learning e videoconferenza. Sono importanti ottime abilità comunicative capacità di spiegare concetti complessi in modo chiaro gestione del gruppo classe e attitudine all’ascolto delle esigenze delle persone partecipanti. Costituiscono titolo preferenziale laurea in discipline tecnico-economiche o giuridiche esperienza pregressa come docente o formatore padronanza degli strumenti digitali e disponibilità all’aggiornamento continuo. Retribuzione €2000 - €3500 all'ora Ore previste non meno di 8.0 a settimana Sede di lavoro Da remoto

New 9 min(s). ago 🔥 View
Cyber Defense Automation Engineer, Mandiant, Google Cloud
Google

Remote Italy

[Italy Applicants] Google welcomes people with disabilities. Note By applying to this position you will have an opportunity to share your preferred working location from the following Remote locations Netherlands Austria Denmark Italy. Minimum qualifications Bachelor's degree in Computer Science Information Systems Cybersecurity or a related technical field or equivalent practical experience. 3 years of experience configuring and maintaining Security Orchestration Automation and Response (SOAR) Technologies as part of Security Engineering System Administration or a similar role. Experience managing and maintaining SOAR platforms and its dependencies and working with/integrating APIs into automation playbooks. Preferred qualifications One or more of the following CompTIA Security+ CompTIA Network+ CISCO (CCNA) ISC2 (CISSP) SANS (GSEC GCIH GCED GCFA GCIA GNFA GPEN). 3 years of experience working with SOC/CSIRT or other incident response related teams. Experience with Security Information and Event Management (SIEM) solutions (e.g. writing queries searches alerts dashboards). Experience scripting (e.g. Python). Understanding of security controls for common platforms and devices (e.g. Linux network equipment) web-based Application Programming Interfaces (APIs) and RESTful programming (e.g. Python libraries) and data manipulation and analysis libraries (e.g. Pandas). Understanding of the incident response containment and remediation process. About the job - As a Cyber Defense Automation Engineer you will be responsible for enabling the technology and tools required to effectively automate and orchestrate daily tasks within a Cyber Defense Center (CDC). You will collaborate with multiple cross-functional teams like Mandiant Architects Mandiant Analysts Client Information Technology (IT) resources and other business resource owners to define requirements and deliver recommendations focused on technologies processes scripting and improvements required to support automation tasks within a CDC. In this role you will work as a member of a highly technical team in a rapidly changing environment administer a variety of information security technologies learn new emerging technologies and be passionate about protecting customer data and corporate assets from the threats facing multiple industries. Part of Google Cloud Mandiant is a recognized leader in dynamic cyber defense threat intelligence and incident response services. Mandiant's cybersecurity expertise has earned the trust of security professionals and company executives around the world. Our unique combination of renowned frontline experience responding to some of the most complex breaches nation-state grade threat intelligence machine intelligence and the industry's best security validation ensures that Mandiant knows more about today's advanced threats than anyone. Individual pay is determined by factors including job-related skills experience and relevant education or training. Austria €88000 - €90000 (EUR) + 15% bonus target + equity + benefits Denmark kr.780000 - kr.800000 (DKK) + 15% bonus target + equity + benefits Italy €66000 - €67500 (EUR) + 15% bonus target + equity + benefits Netherlands €92000 - €94000 (EUR) + 15% bonus target + equity + benefits Learn more about benefits at Google.Responsibilities Identify challenges in customer Cyber Defense Centers and formulate strategies for improvement identify candidates for automation plan implementation of improvements and execute/oversee plans to completion. Advise on technologies relied upon by the client CDC CSIRT and SOC. Provide expertise for SOAR and other SOC technologies that assist in incident response. Create and modify SOAR playbooks written in Python. Engage and collaborate with client stakeholders and other groups within customer environment to drive resolution for security issues. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race color ancestry religion sex national origin sexual orientation age citizenship marital status disability gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation please let us know by completing our Accommodations for Applicants form.

New 9 min(s). ago 🔥 View

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