{"id":62100,"url":"https://alion.io/job/cyera-ml-engineer","title":"ML Engineer","company":{"id":17615,"name":"Cyera","domain":"cyera.com","url":"https://alion.io/company/cyera-com","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","truth_index":{"grade":"A","score":88,"open_postings":6,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":40,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Argo Workflows","optional":true},{"name":"BentoML","optional":true},{"name":"Kubeflow","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-08-18T11:02:11Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-16T20:02:52Z","board_verified":true,"closed_at":null,"days_open":59,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":59},"description":"Description\nAbout Cyera\nCome join the company building the security operating model for the age of AI. AI has changed how data is used - and security must change with it. Cyera's mission is to empower businesses to accelerate AI Adoption by defining a holistic approach to securing AI - from data to access to model. Instead of perimeter controls and static policies, Cyera provides a unified control plane that understands relationships between data, access, and behaviors across humans, systems, and AI. Backed by the world's leading investors and working with a large and growing list of Fortune 1000 companies, we are looking for world-class talent to join us as we usher in the new era of data and AI security.\nAbout the Team\nThe ML Engineering team designs and builds the infrastructure and platforms that enable researchers and engineers to develop, evaluate, deploy, and operate ML models and LLM-based features at scale. We own the systems behind the ML lifecycle - from GPU-based training and evaluation workflows to model serving, LLM infrastructure, observability, internal SDKs, and developer tooling. Our internal platforms are used across multiple teams at Cyera, while the production services we enable power ML capabilities used across our customer base. We work closely with research, engineering, and DevOps teams, taking solutions from early ideation and design through experimentation and into reliable production systems. We value technical depth, ownership, curiosity, and a holistic approach to solving problems.\nAbout the Role\nWe're looking for a strong and curious ML Engineer to join our team and help design, build, and operate the infrastructure behind our machine learning and LLM systems.\nIn this role, you'll work across different parts of the ML lifecycle, from building evaluation workflows and improving CI/CD processes to operating production ML services and evolving our LLM infrastructure. You'll work closely with researchers, data scientists, backend engineers, and DevOps engineers to build infrastructure that enables ML solutions to move efficiently from research and experimentation to production. We're looking for someone with a holistic engineering mindset who enjoys understanding problems end-to-end - the business need, ML requirements, surrounding systems, and production environment - and turning them into reliable technical solutions.\nWhat You'll Do\nDesign and build ML workflows for model training, large-scale evaluation, batch prediction, and experimentation, enabling researchers and data scientists to iterate efficiently and reliably.\nDevelop ML infrastructure and developer tooling by automating recurring processes, improving CI/CD and model build flows, and creating tools that simplify ML development and operations.\nOperate and improve production ML services, strengthening monitoring, observability, reliability, and performance across our ML and LLM systems.\nDeploy and operate ML workloads on Kubernetes and cloud infrastructure, including GPU-based training, evaluation, and inference workloads.\nContribute to our LLM platform, including self-hosted model serving, LLM gateways, internal SDKs, evaluation and observability infrastructure, and integrations with different model providers.\nOwn engineering problems end-to-end, from ideation and understanding business and technical requirements through system design, implementation, deployment, and production operation.\nCollaborate across research and engineering teams to build scalable infrastructure that enables ML solutions to move from experimentation into production.\nExplore, evaluate, and integrate emerging technologies across ML infrastructure, LLMs, and agentic systems to continuously improve our platform and workflows.\nRequirements\nWho You Are\nYou have 4+ years of experience in ML engineering, software engineering, backend engineering, or a similar hands-on engineering role.\nYou have a B.Sc. in Computer Science, Software Engineering, or a related technical field, or equivalent significant practical experience.\nYou have strong Python and software engineering skills, with experience designing and building maintainable, production-quality systems.\nYou have hands-on experience with Kubernetes and Docker, including deploying, debugging, and operating production workloads.\nYou have strong experience with cloud environments such as AWS, GCP, or Azure and understand the infrastructure around running distributed production services.\nYou have experience with ML infrastructure and the ML lifecycle, including areas such as orchestration, model serving, evaluation, training, or production inference.\nYou have experience with CI/CD, observability, and production operations, and understand what it takes to make services reliable beyond simply deploying them.\nYou excel at solving complex problems end-to-end, independently understanding the business context, requirements, and surrounding systems and driving solutions from design through production.\nYou collaborate effectively across research and engineering disciplines and can communicate technical decisions clearly.\nYou are technically curious and proactive, with the ability to dive into unfamiliar technologies and understand how systems work under the hood.\nNice-to-Haves\nYou have experience with GPU workloads or high-scale ML inference, including technologies such as vLLM, BentoML, Argo Workflows, Kubeflow, or similar.\nYou have experience with LLM platforms and infrastructure, such as LLM gateways, evaluation/observability tooling, self-hosted models, or internal LLM SDKs.\nYou have experience with agentic systems, including building agents, memory layers, agent evaluation, or related infrastructure.\nWhy Join Us?\nAt Cyera, we own what we build and how we work. Cyerans are empowered to take initiative, move quickly, and turn ideas into impact. We push boundaries by challenging the status quo, learning fast, and continuously raising the bar - for ourselves and for the industry. We elevate together by lifting each other up, celebrating wins as a team, and recognizing that our success is shared. Feel free to apply even if your experience doesn't tick every box. 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