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# Machine Learning Engineer

- **Company:** MIND DETECT MIND MUSIC GROUP, LLC
- **Location:** Capetown, CA, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), BigQuery, Cloud Computing, Databases, Continuous Integration, Python (Programming Language), PostgreSQL, Machine Learning, Graphics Processing Unit (GPU), Pytorch, Evaluation Pipelines, Large Language Models, Deep Learning, Backend, Fastapi, Kubernetes, HuggingFace, SGLang, Apache Kafka, Milvus, Graphql, Machine Learning Operations, Vertica, Terraform
- **Published:** October 29, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pvble96lgj

## About the Role

* Strong Python and software engineering fundamentals: testing, CI/CD and code others can maintain
 * Experience deploying and running ML models in production
 * Solid PyTorch and deep learning knowledge, enough to work alongside researchers
 * Comfortable with backend services, databases and cloud infrastructure
 * A generalist who's happy switching between ML, backend and infra work
 * High EQ, low ego, and happy in an early-stage environment
 * Nice to have: Ray, Kubernetes, GPU performance work, vector databases, GraphQL, Kafka or LLM serving.

Stack

 * ML: PyTorch, Hugging Face, JAX/Flax, Ray (Train, Serve, Data), SGLang, MLflow
 * Backend: FastAPI, GraphQL (Strawberry), Prefect, Kafka
 * Data: DVC, BigQuery, dbt, Polars, Milvus, Postgres, ClickHouse
 * Infra: GKE with GPUs, Helm, Skaffold, Terraform, Nix, uv

## Description

Your focus will change from week to week. One week you might be deploying a new model from the ML researchers or speeding up inference, the next adding an API feature for a customer or fixing a pipeline. You'll work closely with the leadership team and own the parts of the system you build.

We're searching for bright minds to help bring novel zero-shot discovery models from research to production. Ultimately, you'll bridge the gap between cutting-edge ML research and scalable, reliable systems.

Responsibilities

 * Help take new models from research to production, and keep GPU inference fast and cost-effective
 * Build and maintain the pipelines that ingest and embed merchant catalogues
 * Build features in GraphQL API, which merchants and developers integrate with
 * Look after Kubernetes infrastructure on GCP, including deployments and observability
 * Keep training and evaluation pipelines reproducible
 * Self-host and run LLMs for judging, annotation and evaluation