Machine Learning Engineer [33318]

Stealth Startup; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Stealth Startup is seeking a Machine Learning Engineer to design, deploy, and scale AI-powered products with strong backend engineering skills. You will work with cross-functional teams to ship production-grade ML features for real-world users.

You will build data pipelines, APIs, and scalable services, optimize models for latency and reliability, and maintain high-quality code. 1–2 years of experience and a solid foundation in ML frameworks are expected.

Qualifications

• 1–2 years of professional software engineering or machine learning experience.
• Bachelor's, Master's, or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Strong programming skills in Python and/or Java, Go, or C++.
• Experience building backend services using modern frameworks (FastAPI, Flask, Django, Spring Boot, etc.).
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Familiarity with REST APIs, distributed systems, and cloud platforms (AWS, GCP, or Azure).
• Experience with SQL/NoSQL databases and version control (Git).

Responsibilities

• Design, develop, and deploy machine learning models into production.
• Build and maintain scalable backend services and APIs that power AI applications.
• Develop data pipelines for training, inference, and model evaluation.
• Optimize model performance, latency, and reliability in production.
• Collaborate with product, infrastructure, and software engineering teams to deliver end-to-end AI features.
• Monitor production systems and continuously improve model accuracy and backend performance.
• Write clean, maintainable, and well-tested code.

Skills

Python

Java

Go

C++

PyTorch

TensorFlow

JAX

REST APIs

Distributed systems

Git

SQL/NoSQL

Problem-solving

Communication

Education

Bachelor's, Master's, or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field

Tools

Docker

Kubernetes

CI/CD

MLOps tools

Job description

We are looking for a Machine Learning Engineer with strong backend engineering skills to help build and scale AI-powered products. This role is ideal for engineers with 1–2 years of professional experience who have hands‑on experience developing machine learning solutions while building reliable, production‑grade backend systems.

You’ll work closely with engineering and product teams to design, deploy, and optimize ML-powered applications that serve real‑world users at scale.

Responsibilities

• Design, develop, and deploy machine learning models into production.
• Build and maintain scalable backend services and APIs that power AI applications.
• Develop data pipelines for training, inference, and model evaluation.
• Optimize model performance, latency, and reliability in production.
• Collaborate with product, infrastructure, and software engineering teams to deliver end-to-end AI features.
• Monitor production systems and continuously improve model accuracy and backend performance.
• Write clean, maintainable, and well-tested code.

Qualifications

• Bachelor's, Master's, or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 1–2 years of professional software engineering or machine learning experience.
• Strong programming skills in Python and/or Java, Go, or C++.
• Experience building backend services using modern frameworks (FastAPI, Flask, Django, Spring Boot, etc.).
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Familiarity with REST APIs, distributed systems, and cloud platforms (AWS, GCP, or Azure).
• Experience with SQL/NoSQL databases and version control (Git).
• Strong problem-solving and communication skills.

Preferred Qualifications

• Experience deploying ML models in production.
• Knowledge of LLMs, generative AI, or NLP applications.
• Experience with Docker, Kubernetes, CI/CD, and MLOps tools.
• Familiarity with vector databases, model serving, or distributed training.
• Startup experience or experience working in fast‑paced engineering environments.