Principal Machine Learning Engineer, ML Platform

About this role

Shipping & handling responsibilities 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. Your shipping 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. What is in the Shippo Package? 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 Compensation Our Compensation Ship policy: We believe compensation is a custom experience and are commited to fair and equitable compensation practices. The standard base pay range for this role is min is $212k to a max $287k annual salary (we tend to anchor our offers at the mid point $250k). Since we are focused on hiring Shippos Everywhere, we have 2 US pay ranges, a standard compensation range for the majority of the US and a standard +1 compensation range for those who live in areas where the cost of labor is higher, such as NYC and California. The actual base pay is dependent upon many factors, such as: financial budgets, work experience, training, transferable skills, business needs, and market value. The base pay salary ranges are subject to change and may be modified in the future. Total compensation for this role will include, equity, medical, dental, vision and other benefits noted in our Shippos “package” section. Sail through the process: Here at Shippo, we celebrate inclusivity and are committed to creating equ