Senior Applied AI Solutions Engineer

Team: IT

Location: US

Commitment: Full-time

Workplace Type: remote

Accountabilities

• Build polished prototypes and technical demonstrations across serverless inference, databases, MLflow, MLOps, and applied AI use cases, including Physical AI and healthcare and life sciences.

• Support enterprise customers hands-on through proof-of-concept design, technical onboarding, validation, and the transition from experimentation to production.

• Act as a technical bridge between customer ML teams and the platform, helping diagnose challenges and accelerate time-to-value during the first months of adoption.

• Research emerging applied AI techniques, including new training approaches, inference optimizations, agentic architectures, and frameworks, and turn relevant discoveries into working prototypes.

• Translate research and customer experience into technical write-ups, recommendations, and actionable product feedback.

• Identify recurring issues across customer deployments and provide specific, evidence-based input that can shape the product roadmap.

• Develop reusable technical assets such as notebooks, reference architectures, benchmarks, and implementation guides to reduce onboarding friction.

• Collaborate with sales, product, engineering, and customer teams to ensure technical insights are converted into practical solutions and product improvements.

• Track developments across the AI ecosystem and help the wider team anticipate important applied AI trends over the next 6–12 months.

Requirements

• Strong hands-on experience with modern machine learning systems, including fine-tuning large models, debugging distributed training workloads, building production RAG or agentic pipelines, and optimizing GPU-based inference.

• Fluency across the modern ML technology stack, including PyTorch, Hugging Face, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent platforms, and vector databases.

• Experience working directly with enterprise ML teams, whether in a solutions engineering, customer engineering, ML engineering, or closely related capacity.

• Demonstrated ability to research emerging techniques, read technical and academic papers, and translate relevant findings into working implementations.

• Strong understanding of ML infrastructure and the ability to troubleshoot complex workloads across the stack.

• Excellent communication skills, with the ability to adapt technical explanations for audiences ranging from ML engineers to senior technology and business leaders.

• Strong customer orientation combined with the ability to maintain technical depth and engineering rigor.

• Ability to work effectively in a highly varied environment, moving between customer problems, technical research, rapid prototyping, and product feedback.

• Experience in Physical AI, robotics, simulation, healthcare and life sciences, drug discovery, medical imaging, clinical NLP, or enterprise AI application development is an advantage.

• Familiarity with large-scale MLOps technologies such as Kubeflow, Metaflow, Argo, or Ray is a plus.

• Previous experience at a cloud provider or AI infrastructure company is beneficial.

• Publicly shared technical work, such as useful notebooks, talks, technical articles, or open-source contributions, is a strong plus.

Benefits

• Competitive compensation, with the advertised base compensation range of $200,000–$350,000 USD, depending on experience, skills, qualifications, level, and location.

• Comprehensive benefits package.

• Flexible working arrangements with significant ownership and autonomy.

• Professional growth, continuous learning, and career development opportunities.

• Opportunity to work on impactful AI and ML infrastructure projects.

• Collaborative, innovative, and fast-moving working environment.

• International environment with talented teams distributed across multiple locations.

• Opportunity to work directly with enterprise customers and influence product direction.

• Exposure to emerging areas of applied AI, including generative AI, agentic systems, MLOps, inference optimization, Physical AI, and healthcare applications.

• An environment that values initiative, technical excellence, trust, and meaningful ownership.

• Inclusive workplace committed to equal employment opportunities and a diverse, supportive culture.