Machine Learning Infra 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.

What You'll Do Our infra engineers put petabytes of radio spectrum data at researchers’ fingertips and enable fast, scalable model training across the organization. You’ll design and build core infrastructure from scratch using technologies that you actually want to use. We’re looking for experienced candidates who have strong opinions about tooling best practices and can make informed, future-proof decisions about architecture design.

Possible projects include:

• Scaling our distributed data storage and writing Python APIs that make loading 30GB datasets feel instantaneous

• Setting up the orchestration for model training on GPU clusters, versioning, and artifact deployment

• Exploring creative ways to combine relational and vector-based search queries, enabling researchers to discover the most relevant data for any modeling task

Who We're Looking For • Experience designing and implementing data infrastructure from scratch, including databases, cloud storage, and cloud compute

• Experience managing a production-grade Python codebase that was used by other people

• Experience with AWS, including AWS networking, S3, Sagemaker, RDS, ECS, Lambda, and related infra-as-code tools

• Experience designing database schemas, metadata states, and software abstractions that promote clarity and generalize well to new situations

• Experience working directly with researchers and using infrastructure that supports experiment tracking, model versioning, and artifact deployment, such as MLflow or similar

• You know how to deal with larger-than-memory data inexpensively, without setting up a cluster

• You can write clearly

• Extremely collaborative attitude and interest in helping define large areas of our engineering roadmap

Bonus points• Understanding of database internals (indexes, query optimizers) and data storage formats, and the ability to use it to make practical design decisions

• Experience writing production Rust or C++

• Experience with modern DataFrame libraries and database systems, including Polars, Ibis, Duckdb, or similar

• Experience with maintaining a versioned Python package, related CI/CD best practices, and the Python packaging ecosystem

• Experience with event streaming data systems like ZeroMQ, Kafka, Flink, or similar

• Experience with orchestration frameworks like Airflow, Prefect, Dagster, or similar

• Experience dealing with role-based access to AWS and permissioning

• Experience running distributed jobs using Spark, Ray, Dask, or similar

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