Founded in 2013, Shippo is a logistics and supply company that provides shipping services to retailers, ecommerce platforms, marketplaces, and more. Operating f
Principal Machine Learning Engineer, ML Platform
Location
Hawaii + 6 moreAll locations: Hawaii | Nevada | New Mexico | Ohio | Oregon | Virginia | West Virginia
Posted
104 days ago
Salary
$212K - $287K / year
Seniority
Lead
Job Description
Principal Machine Learning Engineer, ML Platform
Shippo
• 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.
Job 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.
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.
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