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.

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

#LI-CL1