Вакансія Principal Software Engineer

Skills

• Generative AI
• Docker
• Kubernetes
• AWS
• GCP
• Azure
• OpenShift
• Python
• API
• Terraform
• Pulumi

Опис вакансії

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future.
As a Principal Software Engineer for Generative AI at DataRobot, you will be the technical anchor for our GenAI Tooling and Systems teams, shaping the architecture, ensuring scalability, and defining the future direction of our AI platform.
This is not just a hands-on coding role; it’s a technical leadership position where you’ll drive multi-year projects, mentor senior engineers, and ensure our Generative AI solutions deliver real business impact.
You’ll work together with research, engineering, and product, translating cutting-edge AI advancements into robust system capabilities that power real-world applications. This role is for you if you’re passionate about LLMs, Agents, AI Apps, AI orchestration, and scalable tooling, and thrive on solving open-ended, cross-functional, ambiguous challenges.

Key Responsibilities:

• Technical Vision
  • Shape the long-term technical strategy for Generative AI at DataRobot, ensuring our systems are scalable, maintainable, and aligned with business goals.
  • Lead architectural decisions for GenAI tooling (e.g. Agents and Agentic Workflows, Prompt Management, Frameworks & Libraries, LLM Onboarding,Tools, Multi-Agent Evaluations, Multimodality, etc.) and GenAI systems (e.g. Inference optimization, Distributed Training, Finetuning, Distillation, LLM Moderation and Guardrails, etc.).
  • Anticipate technical risks and propose mitigation strategies before they become roadblocks.
• Execution & Impact
  • Hands-on development: Build, ship and operate critical AI infrastructure, balancing rapid iteration with long-term technical health.
  • Solve cross-pillar challenges, such as optimizing LLM latency/cost, improving Agentic workflows, RAG workflows, or ensuring reproducibility in generative outputs.
  • Drive operational excellence by improving observability, reliability, and performance of AI systems in production.
• Leadership & Mentorship
  • Act as the technical advisor to engineering leadership (VPs, Directors) and product teams, influencing roadmap priorities.
  • Mentor Staff/Senior Engineers, elevating the team’s technical bar through design reviews, best practices, and knowledge sharing.
  • Foster a culture of innovation and rigor, ensuring AI solutions are both cutting-edge and production-ready.
• Cross-Functional Collaboration
  • Partner with Research, Product, and GTM teams to align technical efforts with customer needs and market opportunities.
  • Communicate complex technical concepts to executives and non-technical stakeholders, enabling data-driven decisions.

Knowledge, Skills & Abilities:

• A technical leader with 8+ years of software engineering experience, including 3+ years in AI/ML systems (Generative AI preferred).
• Deep expertise in:
  • Scalable systems: Distributed computing, containerization (Docker/K8s), and multi-cloud deployments (AWS/GCP/Azure/Openshift).
  • Software craftsmanship: Python, API design, and infrastructure-as-code (Terraform, Pulumi).
• Proven track record of delivering multi-year, high-impact projects with cross-functional dependencies.
• A systems thinker who balances innovation with operational pragmatism (e.g., cost-to-serve, technical debt).
• Exceptional communication and influence skills – you can rally teams behind a technical vision.

Nice to Have:

• Deep expertise in Generative AI stack: LLMs (open/closed-source), fine-tuning, RAG, orchestration, and evaluation metrics.
• Experience with MLOps tools and AI-specific infra (e.g., vector DBs, GPU optimization).
• Contributions to open-source AI projects or published research in ML/GenAI.
• Background in enterprise SaaS or B2B AI products.