Software Engineer (Machine Learning)

Collinear AI, Inc.
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Compensation
$ 250K

Job location

Tech stack

JavaScript
Artificial Intelligence
Amazon Web Services (AWS)
Program Optimization
Continuous Integration
Database Design
Software Debugging
DevOps
Github
Python
Machine Learning
NoSQL
Open Source Technology
E2e Testing
Next.js
SQL Databases
Web Applications
Web Application Frameworks
Test Driven Development
React
Large Language Models
Backend
FastAPI
Build Management
Kubernetes
Information Technology
Data Management
Front End Software Development
Docker
Jenkins

Job description

We are looking for a talented Software Engineer (Machine Learning) with expertise in React and NextJs (JavaScript frameworks) for frontend development and backend development in Python and Fast API. The ideal candidate will have hands-on experience in DevOps technologies, testing frameworks, database management, and exposure to AI/ML or NLP/LLM projects., * Develop scalable, responsive web applications using modern frontend frameworks (React/Next.js)

  • Design and implement high-performance backend solutions using Python and FastAPI, ensuring reliability and scalability
  • Collaborate with cross-functional teams to define features, enhancements, and deliver product updates
  • Implement and maintain DevOps best practices for continuous integration and deployment using tools like Jenkins, AWS, Docker, and Kubernetes
  • Write and maintain comprehensive unit, integration, and end-to-end tests using testing frameworks
  • Troubleshoot and debug frontend and backend issues, ensuring timely resolution and system optimization.
  • Work with both SQL and NoSQL databases, optimizing queries for efficient data management
  • Collaborate with AI/ML teams to build and deploy applications leveraging NLP and LLM technologies

Requirements

Do you have experience in Web applications?, There are a few specific things we'll be looking for that will help you succeed in this role:

  • Bachelor's or Master's degree in Computer Science/Engineering, or a related field
  • Experience in full stack development with a focus on both frontend and backend technologies
  • Proficiency in JavaScript frameworks (React/Next.js) for frontend development
  • Strong backend development skills in Python (FastAPI) or similar languages
  • Experience with DevOps tools such as Jenkins, AWS, Docker, and Kubernetes for CI/CD pipelines
  • Hands-on experience with testing frameworks and a Test-Driven Development (TDD) approach
  • Expertise in SQL and NoSQL databases, with a solid understanding of database design and optimization
  • Experience in AI/ML or NLP/LLM projects is highly desirable
  • Contributions to open-source projects, with an active GitHub portfolio showcasing innovation and expertise, are preferred
  • Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment
  • Prior experience in top-tier technology companies or startups is a plus

Benefits & conditions

The base salary range for this role in California is $150,000 to $250,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.

Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.

Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.

Compensation Range: $150K - $250K

About the company

At Collinear, we help teams fearlessly ship AI. Frontier labs and AI-native companies use our SimLab to find capability gaps in their agents and generate high-quality data to close them. We believe that the next generation of AI progress won't come from just bigger models, but from more rigorous, long-horizon simulation and programmatic verification. SimLab allows researchers to spin up realistic environments, run agents through complex tasks, and surface failure modes under real-world conditions. We then close the loop by generating targeted synthetic data to retrain models, delivering measurable quality lift on the metrics that actually matter.

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