[All remote jobs](https://jobicy.com/jobs.md)[![Nebius logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/06/d90c0566-221.webp)](https://jobicy.com/company/nebius.md)[Nebius](https://jobicy.com/company/nebius.md) # Senior Applied AI Solutions Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/nebius.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md), [Europe](https://jobicy.com/job-region/europe.md)SalaryUSD 200k–350k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished23 Oct 2026Apply before22 Nov 2026Listing views50Application actions3Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Nebius is seeking a senior applied AI engineer to help enterprise customers move machine-learning workloads from proof of concept to production on its AI cloud platform. The role combines customer-facing technical onboarding with hands-on development of prototypes, demos, reference architectures, benchmarks, and product recommendations. The engineer will work across inference, distributed training, RAG and agentic systems, MLOps, GPU optimization, and cloud-native ML infrastructure. Success depends on translating practical customer issues into reusable assets and concrete product-roadmap improvements. This is a high-impact role for an experienced ML practitioner who can communicate effectively with both engineers and executive stakeholders. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe position requires production-level depth in modern ML systems, including distributed training, GPU inference optimization, MLOps, and enterprise AI implementation. It also demands rapid context switching between customer delivery, experimental prototyping, and influencing product strategy. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$275,000US market range (per year)$190k–$360k0$396.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base compensation range of $200,000–$350,000 USD. Although the posting does not state a cadence, the range is logically inferred as annual base pay based on the senior US-focused professional role and standard pay conventions. The estimated US market range for comparable senior applied AI solutions engineering roles is $190,000–$360,000 USD annually. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied AI](https://jobicy.com/jobs?search_keywords=Applied%20AI.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[Hugging Face](https://jobicy.com/jobs?search_keywords=Hugging%20Face.md)[CUDA](https://jobicy.com/jobs?search_keywords=CUDA.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Distributed Training](https://jobicy.com/jobs?search_keywords=Distributed%20Training.md)[GPU Inference Optimization](https://jobicy.com/jobs?search_keywords=GPU%20Inference%20Optimization.md)[Retrieval-Augmented Generation](https://jobicy.com/jobs?search_keywords=Retrieval-Augmented%20Generation.md)[Agentic AI](https://jobicy.com/jobs?search_keywords=Agentic%20AI.md)[Vector Databases](https://jobicy.com/jobs?search_keywords=Vector%20Databases.md)[Technical Customer Onboarding](https://jobicy.com/jobs?search_keywords=Technical%20Customer%20Onboarding.md) Sample interview questionsDescribe a production ML workload you moved from a proof of concept to deployment. What were the key technical and operational challenges?I would outline the workload, success metrics, deployment architecture, and the trade-offs made around latency, cost, reliability, and observability. I would also explain how I validated the model, automated delivery, monitored production behavior, and incorporated user feedback after launch. How would you diagnose poor inference performance for a large language model running on GPU infrastructure? I would first profile end-to-end latency and throughput to separate queueing, tokenization, network, and GPU execution issues. I would then evaluate batching, model precision, memory use, KV-cache behavior, GPU utilization, kernel efficiency, and serving configuration before testing targeted optimizations such as quantization or parallelism changes. How do you turn repeated customer POC issues into actionable product feedback? I would document the failing workflow, environment, expected behavior, business impact, and a reproducible example. After identifying patterns across customers, I would prioritize them with product and engineering using frequency, severity, revenue impact, and implementation effort, then validate the delivered change with affected users. What trade-offs would you explain when discussing activation checkpointing with an ML engineer and a CTO? For the ML engineer, I would explain that activation checkpointing reduces memory pressure by recomputing activations during backpropagation, trading additional compute time for the ability to train larger models or use larger batches. For the CTO, I would translate that into infrastructure cost, training duration, capacity utilization, and the business value of enabling a workload that would otherwise not fit. How would you design a reusable reference architecture for an enterprise RAG application? I would define the target use cases, data-security boundaries, ingestion and chunking strategy, embedding and retrieval evaluation, model-serving layer, guardrails, observability, and feedback loops. I would package the design as deployable infrastructure, notebooks, benchmark guidance, and operational runbooks so teams can adapt it without rebuilding core components. Opportunity details ## About this role. About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role AI is moving faster than any single product team can track. Nebius is expanding across serverless, databases, MLflow, MLOps, Physical AI, and HCLS — and customers arriving with complex, real-world ML workloads need more than documentation. This role exists to close that gap: someone who can prototype what’s possible, accelerate customers through their first 90 days, and feed hard-won field insight back into the product roadmap. This role sits at the intersection of deep ML engineering and product impact. You’ll spend roughly half your time in the field — helping new customers move from POC to production, running technical onboarding, and working hands-on through their ML stack. The other half you’ll spend building — prototyping applied AI use cases that show what’s possible on the platform, going deep on emerging techniques before they’re mainstream, and turning that expertise into concrete product direction. This is not a presales role. You get your hands dirty every day. What success looks like in 12 months * The product and sales teams have a library of working, polished demos they reach for on calls * Enterprise customers you’ve touched have meaningfully faster time-to-value than those you haven’t * At least 2–3 product changes were shipped because of feedback you originated * The team understands where applied AI is heading 6–12 months from now, partly because you told them Your responsibilities will include: * Build prototypes and demos across the product portfolio — serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS — that become assets for sales, product, and engineering teams * Support new customers hands-on through POC design, technical onboarding, and validation; act as the bridge between their ML team and the platform during the critical first months * Go deep on emerging applied AI — new training techniques, inference optimizations, agentic architectures, new frameworks — and turn findings into working prototypes, writeups, and product recommendations * Feed the product roadmap with specific, grounded feedback; be the voice of “here’s what broke in three customer POCs last month and here’s what needs to change” * Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale We expect you to have: * You’ve fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure — not just read about it * You’re fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases * You’ve worked with enterprise ML teams — whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers * You read papers and implement them — not for credit, but because it’s how you stay sharp * You communicate with calibration: you can explain activation checkpointing tradeoffs to an ML engineer in the morning and the cost implication to a CTO in the afternoon It will be an added bonus if you have: * Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development * Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray) * Prior work at a cloud provider or AI infrastructure company * You’ve shared technical work publicly — notebooks, talks, blog posts that people actually use Who thrives here You’ll thrive here if you’re energized by variety — one day deep in a customer’s MLOps stack, the next building a demo from scratch. You want your technical depth to influence product decisions, not just close deals. What we offer * Competitive salary and comprehensive benefits package. * Opportunities for professional growth within Nebius. * Flexible working arrangements. * A dynamic and collaborative work environment that values initiative and innovation. We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us! Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $200—$350,000 USD Benefits & Perks: * Competitive compensation * Career growth and learning opportunities * Flexibility and ownership * Collaborative and innovative culture * Opportunity to work on impactful AI projects * International environment and talented teams What’s it like to work at Nebius: Fast moving – Bold thinking – Constant growth – Meaningful impact – Trust and real ownership – Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. 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