Software Engineer, MLOps - Machine Learning

17 days ago

H1B sponsor

Base Salary

$162k - $216k/yr

Responsibilities

Build automated MLOps capabilities for model monitoring, retraining, redeployment, champion/challenger testing, A/B testing, drift detection, experiment tracking, and model lifecycle management.
Develop, deploy, monitor, maintain, and iterate on machine-learning models, including scalable batch-prediction and real-time workflows.
Create reliable, reusable, self-serving ML infrastructure and reduce manual intervention in model development and operations.
Design and maintain distributed systems for data-intensive and machine-learning workloads, including batch processing, caching, data movement, and cloud-native infrastructure.
Improve the scalability, performance, reliability, and operational quality of production ML infrastructure.
Integrate the ML platform with Baton’s transportation management platform and replace manual integration workflows with scalable infrastructure.
Collaborate with engineers and cross-functional stakeholders across software engineering, ML development, infrastructure, and production operations.

Advanced production-grade Python proficiency at an L4 or L5 level and experience writing reliable software across modeling, infrastructure, and automation workflows.
Experience working in an environment where production code directly impacts operations.
Strong background in distributed computing, scalable ML infrastructure, and high-performance engineering.
Experience building or maintaining systems that support data-intensive and machine-learning workloads, including big-data systems, batch processing, caching, and cloud infrastructure.
Experience implementing, deploying, and productionizing machine-learning algorithms.
Hands-on experience with data engineering, distributed training, model monitoring, experiment tracking, retraining, redeployment, serving, and model lifecycle management.
Strong SQL and caching experience.
Preferred experience with Kubernetes and cloud infrastructure, especially AWS.
Familiarity with Kubeflow, Iceberg, Feast, or SageMaker and experience with batch prediction, model serving, distributed training, experiment tracking, caching, or feature stores.
Experience building self-serving infrastructure for ML teams and integrating ML platforms with broader production or operational systems.
Experience in a technically rigorous environment such as a large-scale technology company, infrastructure organization, or high-growth engineering team is preferred.
Logistics, transportation, freight, or supply-chain experience is a plus but not required.

Competitive base salary and cash bonus structure, plus an annual company bonus and long-term incentive plan.
401(k) with matching, medical coverage, dental coverage, and vision coverage.
Employee Stock Purchase Program with a 15% discount to market value.
Hybrid work schedule with remote work on Monday and Friday and office work Tuesday through Thursday.
Full-time employment in a collaborative, tech-forward Hayes Valley office backed by a publicly traded enterprise.

51-200 employees