[Remote] reputed company Machine Learning Engineer, ML Platform

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is on a mission to reputed company every merchant successful through excellent shipping and logistics technology. They are seeking a reputed company Machine Learning Engineer for their ML Platform to build a standardized, production-grade ML platform that enhances model reliability and speeds up product development.

Responsibilities

• Set technical strategy and drive a multi-quarter roadmap for ML platform capabilities reputed company to reputed company’s business priorities
• Own cross-team architecture reputed company, RFCs, and design reviews for ML lifecycle and inference
• reputed company the engineering bar through mentorship, production readiness standards, and reusable platform primitives
• Be accountable for platform adoption, reliability, and cost-performance reputed company
• Build and operate core ML platform components:
• + ML lifecycle reputed company (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-reputed company model serving for reputed company-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 reputed company 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 reputed company’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 reputed company research and production environments by creating repeatable frameworks, shared standards for code quality and reproducibility, and self-serve paths to reputed company models safely
• Collaborate with Data and Engineering teams to ensure the platform supports reputed company workflows, drives adoption, and meets reliability expectations
• Mentor engineers through design reviews, architecture guidance, and shared best practices across platform and ML development

Skills

• 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 reputed company 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 reputed company-time serving, CPU versus GPU, latency and throughput, cost, and operational complexity
• Demonstrated reputed company-level technical leadership, including setting technical direction, driving cross-team alignment reputed company RFCs/design reviews, and delivering multi-quarter roadmaps
• Proven ownership of reliability and operational reputed company 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 (reputed company, SageMaker, reputed company AI, or similar), with clear judgement on strengths, limitations, and build-vs-buy reputed company
• reputed company experience (useful, not required), including reputed company 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

• reputed company coverage for medical, dental, and reputed company (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

Company Overview

Company H1B Sponsorship