Ritual is hiring a Machine Learning Engineer in Engineering — Remote. The overview below is synthesized from the employer posting on job-boards.greenhouse.io: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
About Ritual
Company context from the listing:
Ritual is building a sovereign, execution layer for AI, with our private testnet already live and running. The Ritual chain is the first blockchain custom-built to support AI-native operations, designed from the ground up to enable a new class of applications at the intersection of crypto and AI.
About the role
Day-to-day scope for the Machine Learning Engineer as described in the posting:
The listing frames the Machine Learning Engineer at Ritual as a hands-on product role. In this role, you will work on a variety of projects related to applied machine learning, including working with the product team on automating Data ETL pipelines, deploying machine learning models, and shipping customer-facing products.
- Scale up model inference and running predictions at scale on cutting-edge models
- Work end to end to connect ML models to human interfaces (e.g. APIs, browsers, and applications)
- Designing and implementing large-scale data and ML pipelines through a full end-to-end product development lifecycle
- Collaborate with a cross-functional team of engineers, researchers, product managers, designers, and operations teammates to create cutting-edge products
About you
Company context from the listing:
- Experience as a software engineer
- Experience building and serving machine learning models
- Familiarity with Python and related ML frameworks such as PyTorch, Tensorflow, Jax, and other open-source stacks such as HuggingFace
- Ability to reason through machine learning system tradeoffs
- A high level of end-to-end ownership and self-direction
Extras
- Familiarity with basic machine learning system stacks e.g. TinyML, Triton, CUDA, ROCm, Exo, MLIR, Halide, etc
- Familiarity with DataOps, MLOps, and ML orchestration pipelines
- Understanding of modern ML architectures and intuition for inference performance tradeoffs
- Experience or interest in working on open-source ML products
- Interest in building tech aligned with user privacy, computational integrity, and/or censorship resistance
- Experience at fast-growing companies or startups
Why join us
What the posting highlights about the offer:
- Join a passionate group of engineers, researchers, and operators on a mission to build the next generation of AI infrastructure
- Highly competitive compensation package, including annual discretionary bonus & optimized tax structure compared to the vast majority of web3 startups
- Top 1% of benefits across the web3 startup space
- 100% of premiums covered on highest quality healthcare
- Aggressive company 401k match
- Fully remote and/or hybrid, up to you!
- Participate in virtual and in-person events
- Much much more!
Build a standout application
For the Machine Learning Engineer at Ritual, reviewers look for concise evidence over buzzwords. Mirror the language of the posting sparingly, quantify support or delivery outcomes, and show how you handled ambiguity, time-zone collaboration and user empathy. Keep your resume to impact, keep your cover note to one page, and link to artifacts — tickets resolved, docs shipped, dashboards owned — that prove you can operate in a fast-moving Web3 team. Prepare to discuss a time you turned a confusing user report into a clear fix and how you measure quality in support and operations.
Web3 hiring values reliability: on-time follow-through, clear writing, and a track record of improving runbooks and tooling. Treat the application as a work sample. For interviews, be ready to walk through how you prioritize across time zones, handle a difficult user, and decide when to escalate versus resolve directly. Show how you document decisions so the next teammate benefits.
In a distributed Web3 org, trust builds through written clarity. Use the cover note to demonstrate it. Add links to public work, keep formatting scannable, and close with a clear ask. Hiring managers skim — make impact obvious in the first half-page.
Career growth in Web3 rewards continuous learning. Follow protocol changelogs, practice with testnets, and contribute to open issues. Small, consistent contributions compound into credibility more than one-off credentials.