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Machine Learning Engineers at Gensyn develop scalable ML systems, optimize reinforcement learning pipelines, and collaborate on production projects from theory to implementation.
Machine intelligence will soon take over humanity’s role in knowledge-keeping and creation. What started in the mid-1990s as the gradual off-loading of knowledge and decision making to search engines will be rapidly replaced by vast neural networks - with all knowledge compressed into their artificial neurons. Unlike organic life, machine intelligence, built within silicon, needs protocols to coordinate and grow. And, like nature, these protocols should be open, permissionless, and neutral. Starting with compute hardware, the Gensyn protocol networks together the core resources required for machine intelligence to flourish alongside human intelligence.
Machine Learning Engineers at Gensyn design and build advanced ML systems that bridge cutting-edge research and real-world deployment.
This role spans proof-of-concept research, high-performance ML optimization, peer-to-peer reinforcement learning, and the development of high-scale fault-tolerant distributed training pipelines. You’ll collaborate closely with researchers, systems engineers, fullstack software engineers, and product teams to take ideas from prototype to production.
Responsibilities- Build scalable, distributed ML compute systems that operate efficiently over uniquely decentralised and heterogeneous infrastructure
- Develop and optimize reinforcement learning and applied ML pipelines, improving performance, reliability, and reproducibility.
- Collaborate with both researchers and production engineers to design and run novel experiments, taking research from theory to production.
- Prototype and evaluate new ML architectures, tools, and frameworks to accelerate experimentation and deployment.
- Apply strong software engineering discipline to ensure robustness, observability, and maintainability across ML codebases.
Must have
- Strong background in applied machine learning and/or reinforcement learning, with hands-on experience training, evaluating, and optimizing models.
- Proven experience building or scaling ML systems (pre-training, post-training, inference)
- Comfortable working in an experimental environment, with extremely high autonomy and unpredictable timelines
- Impeccable analytical and problem-solving skills
- Strong software engineering fundamentals: data structures, algorithms, and system architecture.
Preferred
- Experience building highly performant, distributed systems
- Demonstrated experience operationalizing novel ML research, bridging experimentation and production
- Familiarity with decentralized or heterogeneous compute environments and distributed orchestration at scale.
- Experience developing mission-critical, fault-tolerant ML infrastructure
Nice to have
- Experience working in a startup/scaleup environment
- Previous experience working with smart contracts
Compensation / Benefits
- Competitive salary + share of equity and token pool
- Fully remote work - we currently hire between the West Coast (PT) and Central Europe (CET) time zones
- Visa sponsorship - available for those who would like to relocate to the US after being hired
- 3-4x all expenses paid company retreats around the world, per year
- Whatever equipment you need
- Paid sick leave and flexible vacation
- Company-sponsored health, vision, and dental insurance - including spouse/dependents [🇺🇸 only]
Autonomy & Independence
- Don’t ask for permission - we have a constraint culture, not a permission culture.
- Claim ownership of any work stream and set its goals/deadlines, rather than waiting to be assigned work or relying on job specs.
- Push & pull context on your work rather than waiting for information from others and assuming people know what you’re doing.
- Communicate to be understood rather than pushing out information and expecting others to work to understand it.
- Stay a small team - misalignment and politics scale super-linearly with team size. Small protocol teams rival much larger traditional teams.
Rejection of mediocrity & high performance
- Give direct feedback to everyone immediately - rather than avoiding unpopularity, expecting things to improve naturally, or trading short-term pain for extreme long-term pain.
- Embrace an extreme learning rate - rather than assuming limits to your ability / knowledge.
- Don’t quit - push to the final outcome, despite any barriers.
- Be anti-fragile - balance short-term risk for long-term outcomes.
- Reject waste - guard the company’s time, rather than wasting it in meetings without clear purpose/focus, or bikeshedding.
Top Skills
Distributed Systems
Machine Learning
Ml Optimization
Reinforcement Learning
Software Engineering
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