Principal Machine Learning Engineer, ML Platform

Role Overview

• Set technical strategy and drive a multi-quarter roadmap for ML platform capabilities aligned to Shippo’s business priorities.
• Own cross-team architecture decisions, RFCs, and design reviews for ML lifecycle and inference.
• Raise the engineering bar through mentorship, production readiness standards, and reusable platform primitives.
• Be accountable for platform adoption, reliability, and cost-performance outcomes.
• Build and operate core ML platform components: ML lifecycle foundation (experiment tracking, reproducibility, artifact management, model registry, versioning, and controlled promotion workflows using MLflow or equivalent).
• Training and experimentation enablement (standardized environments, reusable pipelines/templates, evaluation harnesses, and repeatable workflows that let data scientists move from exploration to production with confidence).
• Kubernetes-native model serving for real-time inference (safe rollout and rollback, autoscaling, reliability practices, and cost controls).
• Batch inference and scoring pipelines (repeatable backfills, retraining triggers, consistent packaging between training and inference).
• Observability for ML systems (service health metrics, alerting, and model-quality signals such as drift and data quality).
• Developer experience (templates, reference implementations, documentation, and self-service workflows).
• Evaluate and recommend inference frameworks and deployment patterns, and document tradeoffs for Shippo’s workloads.
• Identify and resolve performance bottlenecks across the inference stack (model runtime, compute utilization, networking, serialization, and autoscaling behavior).
• Establish ML engineering standards across training, evaluation, testing, model packaging, CI/CD, production readiness, and incident response.
• Partner with Data Science teams to bridge research and production environments by creating repeatable frameworks, shared standards for code quality and reproducibility, and self-serve paths to deploy models safely.
• Collaborate with Data and Engineering teams to ensure the platform supports real workflows, drives adoption, and meets reliability expectations.
• Mentor engineers through design reviews, architecture guidance, and shared best practices across platform and ML development.

Requirements

• 15+ years of software engineering experience, including ownership of production systems (platform, infrastructure, or distributed systems).
• 4+ years owning ML systems end-to-end in production, including on-call and incident response, and making architecture decisions based on operational constraints (latency, throughput, availability, and cost).
• Strong experience building and running services on Kubernetes, including deployments, autoscaling, and observability.
• Hands-on experience with ML lifecycle tooling such as MLflow or equivalent (tracking, registry, packaging, and promotion workflows).
• Demonstrated ability to evaluate inference tradeoffs across batch and real-time serving, CPU versus GPU, latency and throughput, cost, and operational complexity.
• Demonstrated Principal-level technical leadership, including setting technical direction, driving cross-team alignment via RFCs/design reviews, and delivering multi-quarter roadmaps.
• Proven ownership of reliability and operational outcomes for production systems (SLOs, incident response, and measurable improvements in stability and performance).
• Demonstrated ability to ship incrementally, prioritize production reliability over perfect solutions, and drive adoption through pragmatic platform design.
• Experience working with or evaluating managed ML platforms (Databricks, SageMaker, Vertex AI, or similar), with clear judgement on strengths, limitations, and build-vs-buy decisions.
• Bonus Databricks experience (useful, not required), including Databricks workflows and ML tooling integration.
• Experience with inference and serving frameworks.
• Experience with feature store patterns, online and offline consistency, and model evaluation at scale.
• Experience supporting optimization systems and decision engines in production.
• LLM or agent workflow experience, especially evaluation harnesses, deployment patterns, guardrails, and monitoring.

Tech Stack

• Distributed Systems
• Kubernetes

Benefits

• Healthcare coverage for medical, dental, and vision (90% covered by the company, incl. dependents).
• Pets coverage is also available!
• Take-as-much-as-you-need vacation policy & flexible working hours
• One week-long company wide winter slow down
• 3 Volunteer Days Off (VTOs)
• WFH stipend to set up your home office
• Charity donation match up to $100
• Dedicated programs, coaching, tools, and resources for your professional and career growth as well as an individual learning stipend for your personal and focused growth
• Fun team in person time through our Shippos Everywhere program which includes regular team and company off-sites throughout the year as well as local Shippos gatherings.