Partner with Data Science to package models for deployment and integrate them into our products and internal services.
Implement and improve ML deployment and inference workflows (batch and/or real-time), including automation and CI/CD patterns with guidance from senior engineers.
Build and maintain API endpoints or services that expose model predictions, including input validation, error handling, and documentation.
Write tests (unit/performance/integration) to validate model behavior and service reliability; help create repeatable validation checks and release processes.
Instrument services with logging/metrics and help monitor production behavior; participate in incident triage and troubleshooting with support from the team.
Contribute to performance and cost improvements through profiling and practical techniques like batching, basic caching, and efficiency-minded design.
Stay current on relevant AI/ML engineering best practices and share learnings with the team.

Requirements

Bachelor's or master's degree in Computer Science, Engineering, or related field.
1+ years of experience (including internships/co-ops) building software in a production environment.
Proficiency in Python with a focus on readable, testable code.
Familiarity with core ML concepts and at least one ML framework (e.g., PyTorch, TensorFlow, scikit-learn).
Familiarity with building or consuming APIs (HTTP/JSON) and basic service development patterns.
Comfort working in a collaborative environment: asking questions, communicating tradeoffs, and incorporating feedback.
Willingness to learn cloud, containerization, and MLOps practices as part of day-to-day work.
Preferred Qualifications:
Exposure to MLOps tools or patterns (e.g., MLflow, Airflow, Kubeflow, feature stores, model registries).
Experience with containers (Docker) and/or orchestration (Kubernetes).
Experience with observability tools (e.g., Datadog, Prometheus/Grafana) and production troubleshooting.
Basic performance tuning experience (profiling, async patterns, caching concepts).
Experience working with data platforms (e.g., Snowflake, Spark) or large-scale data pipelines.

Benefits

Flexible paid time off, giving you the opportunity to rest, relax and recharge away from work
14 Paid Company Holidays, includes 2 floating holidays (you choose!)
A comprehensive benefits plan including medical, dental, life, vision, disability, and life insurance covered up to 100% by Payscale
Unlimited infertility coverage benefits through our medical plans
Additional supplemental health benefits offered to you and your family
401(k) retirement program with a fully vested immediate company match
16 weeks of paid parental leave for birthing and non-birthing parents
Health Savings Account (HSA) options and company contributions each pay period
Flexible Spending Account (FSA) options for pre-tax employee allocations
Annual remote work stipend to be used on wellness or home office equipment

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonML frameworksPyTorchTensorFlowscikit-learnAPI developmentunit testingperformance testingintegration testingperformance tuning

collaborationcommunicationfeedback incorporationproblem-solvingincident triage