Easy Apply
Easy Apply
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.
This is a hands-on ML engineering role for someone who wants to build, ship, and improve ML products quickly. You’ll work at the intersection of applied machine learning, experimentation, and product development, turning promising ideas into useful, real-world systems. We’re looking for someone who combines strong ML depth with product instinct, startup urgency, and the ability to operate with high autonomy.
Responsibilities- Own ML projects from early prototype through launch and iteration
- Build and improve applied ML and reinforcement learning workflows that deliver measurable product value
- Design and run fast, thoughtful experiments to validate ideas and guide product and technical decisions
- Explore new models, tools, and techniques that can unlock better product experiences
- Write robust, maintainable code and help raise the engineering quality of ML development
- Collaborate closely across product, engineering, and research to ship practical solutions in ambiguous, fast-changing environments
Must have
- Strong background in applied machine learning and/or reinforcement learning, with hands-on experience training, evaluating, and improving models
- Strong product instinct and judgment around where ML can create real user and business value
- Proven ability to take ML work from prototype to production through rapid experimentation, iteration, and deployment
- Comfortable working in an experimental environment with high autonomy and unpredictable timelines
- Strong software engineering fundamentals and the ability to write clean, reliable, production-quality code
Preferred
- Experience shipping ML-powered product features in a startup or similarly fast-moving environment
- Ability to balance speed and quality, making pragmatic technical decisions in ambiguous environments
- Familiarity with modern ML tooling and workflows for experimentation, model improvement, and productionization
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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