dv01 - MLOps Engineer
Requirements
• 4–7 years of relevant experience in platform engineering, DevOps, or MLOps, with solid experience operating systems in production. • Hands-on experience with ML lifecycle tooling. You've built or operated experiment tracking, model registry, and pipeline workflows using MLflow or similar platforms (e.g., Weights & Biases, Kubeflow, SageMaker, Vertex AI Pipelines). This is core to the role. • Hands-on experience with ML lifecycle tooling. • Strength in cloud-native infrastructure. You're comfortable with Kubernetes, containerized workloads, and infrastructure-as-code tools such as Terraform. • Strength in cloud-native infrastructure. • CI/CD fluency. You've designed and maintained automated build, test, and deployment pipelines, ideally for ML or data workloads. • CI/CD fluency. • Solid Python/Go skills and comfort supporting PyTorch-based production systems (deploying, serving, and operating them, not necessarily authoring the models). • An operations and security mindset. You understand infrastructure security, IAM, secrets management, and operational risk, and you build with secure, reliable defaults. • An operations and security mindset. • Clear communication and collaboration. You work well cross-functionally, can mentor and provide technical guidance, and are comfortable making pragmatic decisions in ambiguous problem spaces. • Clear communication and collaboration. • Experience with GCP • Experience with Pulumi • Experience with GitHub Actions (GHA) • Experience with Go • Experience supporting data engineering platforms, data warehousing, or ETL/ELT operations • Exposure to LLM serving runtimes (e.g., vLLM, llama.cpp) or agentic systems and Model Context Protocol (MCP) servers • Familiarity with ML compiler stacks (e.g., LLVM/MLIR) • Experience designing benchmarking or evaluation frameworks for ML/AI systems • Familiarity with Excel Pivot Tables • In good faith, our salary range for this role is $185,000–$200,000, but we are not tied to it. Final offer amount will be at the company’s sole discretion and determined by multiple factors, including years and depth of experience, expertise, and other business considerations. Our community is fueled by diverse people who welcome differing points of view and the opportunity to learn from each other. Our team is passionate about building a product people love and a culture where everyone can innovate and thrive.
Responsibilities
• Build and operate the ML lifecycle platform. Own the tooling that makes model development reproducible and production-ready, with MLflow (or comparable systems) at the center: experiment tracking, model registry, artifact and metadata management, and versioned, repeatable training and inference pipelines. • Build and operate the ML lifecycle platform. • Own CI/CD and deployment for ML workloads. Build automated pipelines that move models from notebook to production safely, including packaging, containerization, automated testing and validation, staged rollouts, and rollback. • Own CI/CD and deployment for ML workloads. • Make models observable and reliable in production. Stand up monitoring for model and service health, including latency, drift, data-quality, and cost signals, with alerting and clear runbooks so issues surface and resolve quickly. • Make models observable and reliable in production. • Build the cloud-native foundations. Contribute to and manage containerized workloads on Kubernetes and codify infrastructure with infrastructure-as-code tooling such as Terraform, keeping environments consistent, secure, and reproducible. • Build the cloud-native foundations. • Establish sensible guardrails. Implement infrastructure-level governance for ML systems, including access controls, deployment policies, and auditability, partnering with security and compliance to align with our risk and regulatory requirements. • Establish sensible guardrails. • Enable and mentor the teams you support. Define repeatable patterns and shared services that reduce friction for data and application teams, provide technical guidance and mentorship to junior engineers, and contribute to the direction of dv01's MLOps practices. • Enable and mentor the teams you support.
Benefits
• Unlimited PTO. Unplug and rejuvenate, however you want—whether that’s vacationing on the beach or at home on a mental-health day. • Unlimited PTO • $1,000 Learning & Development Fund. No matter where you are in your career, always invest in your future. We encourage you to attend conferences, take classes, and lead workshops. We also host hackathons, brunch & learns, and other employee-led learning opportunities. • $1,000 Learning & Development Fund • Remote-First Environment. People thrive in a flexible and supportive environment that best invigorates them. You can work from your home, cafe, or hotel. You decide. • Remote-First Environment. • Health Care and Financial Planning. We offer a comprehensive medical, dental, and vision insurance package for you and your family. We also offer a 401(k) for you to contribute. • Health Care and Financial Planning. • Stay active your way! Get $138/month to put toward your favorite gym or fitness membership — wherever you like to work out. Prefer to exercise at home? You can also use up to $1,650 per year through our Fitness Fund to purchase workout equipment, gear, or other wellness essentials. • Stay active your way! • $1,650 per year through our Fitness Fund • New Family Bonding. Primary caregivers can take 16 weeks off 100% paid leave, while secondary caregivers can take 4 weeks. Returning to work after bringing home a new child isn’t easy, which is why we’re flexible and empathetic to the needs of new parents. • New Family Bonding.
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