Machine Learning Engineer
At Source, we’re building the next generation of AI-driven systems that redefine how people interact with technology. Our mission is to create intelligent, elegant, and scalable solutions that merge creativity with computation — enabling businesses to automate complex workflows, unlock insights, and deliver exceptional digital experiences.
We’re a company of makers — engineers, designers, and thinkers — who believe that AI should amplify human potential, not replace it. Every model we build, every pipeline we optimize, and every product we release is a step toward that vision. We combine deep technical expertise with thoughtful product thinking to turn ambitious ideas into real-world impact.
For a driven and curious engineer, Source offers a rare opportunity to work at the intersection of applied research and production-grade engineering. Here, you’ll build systems that learn, adapt, and evolve — and you’ll grow alongside some of the most talented minds in the industry.
What we look for
Technical depth. You have strong foundations in machine learning, statistics, and data engineering. You understand how models work under the hood, and you can move fluidly between theory and implementation.
Applied problem-solving. You know that machine learning isn’t just about algorithms — it’s about solving real business problems. You can design, train, and deploy models that deliver measurable value.
Code craftsmanship. You write clean, modular, and efficient code in Python (and possibly C++ or Rust). You care about testing, maintainability, and performance.
Curiosity. You stay up to date with the latest ML techniques and frameworks — from transformers and reinforcement learning to efficient inference and model compression — and know when (and when not) to apply them.
Collaboration. You work seamlessly with data scientists, backend engineers, and product designers. You communicate clearly and value diverse perspectives that make the product stronger.
What we offer
High-impact projects. You’ll work on systems that power automation, recommendation, and intelligence for top-tier brands — from prototyping new models to scaling them in production.
Growth through challenge. Our engineers own full lifecycles — from research and experimentation to deployment and optimization. You’ll have freedom to explore new techniques and push your technical limits.
Competitive compensation. We offer market-leading pay, performance-based bonuses, and potential equity participation — because we believe in shared success.
A culture of innovation. We’re remote-first, globally distributed, and deeply collaborative. We prioritize learning, experimentation, and craftsmanship. Expect hack weeks, learning stipends, and plenty of room to explore your ideas.
The selection process
Our hiring process is designed to uncover potential, not just credentials. After an initial screening, you’ll complete a practical challenge focused on model design, implementation, and evaluation — similar to the work you’ll do at Source. From there, we’ll invite you to technical and cultural interviews to ensure a strong fit on both sides.
We know great engineers come from diverse backgrounds. Even if you don’t meet every requirement, we encourage you to apply if you believe you can grow into the role.
Before you apply
If you’ve applied to Source before, we recommend waiting six to twelve months before reapplying to give yourself time to grow and strengthen your portfolio.
We move fast, expect excellence, and invest heavily in our team’s development. If you’re looking for a place where you can experiment fearlessly, learn continuously, and make meaningful contributions to the future of AI, Source is the place to do it.
If this sounds like you — apply now. We can’t wait to meet you.