Machine Learning Engineer

DS creates systems that power the next generation of radio spectrum intelligence. We collect radio data from all over the world, train neural networks to decipher it, and run them on the smallest chips we can. We’re solving a new, technically hard problem where nothing from other fields works out of the box, and along the way, we’ve built our own stack from scratch, including entirely new embedding model architectures, custom GPU kernels, and much more.

This year we’ve done 8-figure revenue, tripled our headcount, and raised a $25M Series A led by Sarah Guo (Conviction), Akhil Iyer (Shield Capital), and Nat Friedman (NFDG). We’re using it to invest in new research and to grow our small, New York-based team across engineering, research, and product.

Joining DS means owning major parts of a fast-growing AI research organization, joining a collaborative, talent-dense team with decades of experience in probabilistic ML, accelerated computing, embedded systems, and signal theory, and growing your career in the areas that interest you. You’ll fit in if you want to come to work for the problem itself and don’t want to choose between technical rigor, business value, and real-world impact. We work with high ownership and trust, and we do it together in the office 5 days/week.

We’re hiring a Machine Learning Engineer to take ownership of developing and optimizing creative models for understanding the radio spectrum. You’ll be responsible for translating high-level problem statements into working machine learning solutions—owning the entire process from research to implementation. Your work will have an immediate impact, driving critical improvements to our product and delivering real results in the field.

This role requires someone with incredibly strong fundamentals in math, statistics, and programming with the creativity to find the best solutions in a novel domain. You’ll work closely with embedded engineers, leadership, and our customers to ensure that our models not only work in theory but perform in the real world.

What You'll Do

• Given a well-defined (but difficult) problem, an objective function, and guidance on possible research areas, you’ll identify, develop, and implement the best ML solution.

• Own the entire machine learning pipeline—research, data management, model development, and testing.

• Build proofs-of-concept, validate them with customers in the field, and rapidly iterate based on feedback.

• Partner with collaborators on the MLE team to continuously develop and refine ML practices and infrastructure.

• Continuously optimize and refine models to improve performance, accuracy, and efficiency.

• Work closely with the embedded systems team to integrate machine learning models into our products.

• React quickly to changing requirements, identifying bottlenecks and adjusting approaches as needed.

• Grow fast with real opportunities – We’ll keep expanding your scope and giving you bigger challenges to help you reach your goals. If you don’t know exactly what role you want to grow into, you’ll have the freedom to take on different responsibilities and find the right path.

Who We're Looking For

• You’ll need to be able to operate in an environment with uncertainty and evolving mission requirements, balancing research with practical implementation.

• Strong experience in machine learning and statistical analysis, with a proven track record of applying research to real-world problems.

• Deep proficiency in Python, PyTorch, and related frameworks, plus familiarity with Linux.

• Ability to work across disciplines—collaborating with embedded engineers, leadership, and customers to refine and deploy solutions.

• Strong communication skills, ensuring complex technical concepts are clearly understood by all stakeholders.

Who Thrives at Distributed Spectrum

• Fast learners over specific backgrounds – We care more about how quickly you can pick up new skills than where you’ve worked before.

• Intellectual honesty – The right answer matters more than being right. You challenge assumptions, test ideas, and pivot when needed.

• Adaptability – We’re organized, but sometimes things change quickly. You find a way to make it work and balance short-term deliverables with long-term goals.

• Ownership of outcomes – You optimize your own time, focus on what matters to deliver quickly, and cut out inefficiencies.

• Not building in a vacuum – You stay connected to the rest of our teams and our customers to make sure all the pieces fit together.

What We Offer

• Above-market salary, equity, and benefits package.

• Early Series A Equity

• Excellent health, dental, and vision coverage

• 401(k) match - up to 4% of your salary

• Flexible PTO

• Daily office lunches in NYC