Job Application for Staff ML Infrastructure Engineer at Engine

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Join Our Journey at Engine

At Engine, we’re revolutionizing work travel. Our modern travel platform isn’t just about booking trips; it’s about transforming how businesses and their teams experience travel. From seamless booking options with top airlines, hotels, and car rental providers to single-invoice billing and flexible trip modifications, we make travel not only easier to manage but also enjoyable. Backed by powerhouse investors like Telescope Partners, Blackstone, Elefund, and Permira, we’re growing fast—and we want you to be part of it.

Your Mission:
The Pricing team is responsible for determining the best price for all hotel rooms, flights, and rental cars sold on the platform. For each good sold we balance turning a profit, passing savings along to our members, and remaining competitive within the market. To those ends, the Pricing team is looking to enhance the performance of our pricing strategies by incorporating ML models into our flow. The ML Infra Engineer will build and scale the platform that powers low-latency, high-throughput ML model inference in production. You’ll design, deploy, and maintain model-serving infrastructure, APIs, and orchestration workflows.

What You’ll Bring to Engine:
We’re looking for someone who’s ready to make an impact and grow alongside us:

Experience:

Model Serving: Deploy and operate machine learning models optimized for low-latency, high-throughput inference in production environments.
API Integration: Build and maintain clean gRPC interfaces to expose model predictions to upstream services.
Application Engineering: Own the production code paths that deliver features to the model—writing maintainable, testable application logic that integrates cleanly with the broader system.
Reliability & Resilience: Implement caching, circuit breakers and graceful fallbacks to isolate ML failures.
Performance Optimization: Tackle cold-start issues, batching strategies, serialization overhead, and memory management.
Observability & Scaling: Instrument metrics (latency, throughput, error rates); configure autoscaling and alerting.
CI/CD & Versioning: Containerize workloads, automate promotions across dev/staging/prod, and manage side-by-side model versions with clear rollback mechanisms.

Technical Skillset:

ML Serving – Hands-on with TensorFlow Serving, TorchServe, or similar frameworks.
Python, gRPC & REST – Build production-grade APIs and integrate model inference into application workflows.
Docker & Kubernetes – Containerize and orchestrate inference services at scale.
Argo CD & Terraform – Manage deployments and infrastructure via GitOps and IaC.
Datadog, Prometheus, OpenTelemetry – Instrument, monitor, and alert on system health.
CI/CD – Automate versioning, testing, and promotion of model-serving pipelines.
Feature Delivery & Model Lifecycle – Experience wiring real-time features (e.g., Redis, Kafka) and managing model versions with tools like MLflow or BentoML

Cash compensation: The base salary range for this role is $190,000 to $240,000. Final compensation packages are determined by various factors, including prior experience and expertise. This role is also eligible to receive equity compensation.

The Engine Edge: Perks & Compensation
We believe in rewarding great work with great benefits:

Compensation: Competitive base pay tied to role and experience, with opportunities for bonuses, commissions, and equity.
Benefits: Check out our full list at engine.com/culture.
Environments for Success: Different roles have different needs in terms of the environments that drive success which is why we have a hybrid-hub model. Whether you are in one of our amazing offices or fully remote, we’ll make sure you have what you need to succeed.

Perks and benefits may vary based on employment type, location, and more.

Ready to Build the Future of Work Travel?
Join us on our mission to transform how work travel works—for businesses, for travelers, and for the industry. Apply now and let’s make travel simpler, smarter, and more enjoyable—together.

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Staff ML Infrastructure Engineer

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