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# Senior Machine Learning Engineer - AWS, Real-Time Inference, Pipelines

- **Company:** TWG, INC.
- **Location:** Santa Monica, CA, United States
- **Experience:** Expert
- **Salary:** $190,000.0 - $290,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Data Infrastructure, Python (Programming Language), Machine Learning, Data Streaming, Data Storage Technologies, Apache Kafka, Stream Processing
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=63cca149d74fc0ff

## About the Role

* Strong data / ML engineering experience with streaming systems (e.g., Kafka / Kinesis / MSK) and modern data storage formats
 * Experience building low-latency, high-throughput inference services
 * Proficiency in a systems language (e.g., Go) alongside Python
 * Production AWS experience
 * Familiarity with financial market data or trading protocols a plus - the US feed is a FIX 5.0 SP2 drop-copy session with real-world quirks (nanosecond timestamps, repeating groups, dedup semantics)
 * Familiarity with chain-data infrastructure (node providers, subgraphs, event indexing) is a plus

## Description

As a Senior ML Engineer for AWS and Real-Time Inference, you'll own the fast path: ingesting live trading data and scoring it in near real time. It's a systems-heavy role focused on streaming, low-latency inference, and the retraining cadence that keeps models current and most directly determines whether the systems keep up with live markets., * Streaming and storage pipelines that feed both model training and low-latency inference.

 * The online inference path and its latency. A real-time detector microservice is built and unit-tested but not yet deployed - it needs to be connected to a run-time model and hold latency under live load. One known, non-trivial problem lives here: batch scoring ranks across a whole population, but single-account (or single-wallet) real-time scoring has no population to rank against, so it must threshold on calibrated raw scores.
 * The model retraining cadence as data and labels accumulate, including drift-triggered retraining.
 * Productionizing new features and detectors on the fast path, in partnership with data science.


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