Machine Learning Engineer

Machine Learning Engineer at Gensyn

Machine intelligence is rapidly evolving to take over humanity’s role in knowledge-keeping and creation. Gensyn is at the forefront of this transformation, building open, permissionless, and neutral protocols for machine intelligence coordination and growth. Starting with compute hardware, the Gensyn protocol networks together the core resources essential for machine intelligence to flourish alongside human intelligence.

As a Machine Learning Engineer at Gensyn, you will design and build advanced ML systems that bridge cutting-edge research with real-world deployment. This dynamic 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 will collaborate closely with researchers, systems engineers, fullstack software engineers, and product teams to transition 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.

Competencies

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 - hiring between the West Coast (PT) and Central Europe (CET) time zones.
• Visa sponsorship available for relocation to the US after hiring.
• 3-4x all expenses paid company retreats around the world, per year.
• Flexible equipment budget.
• Paid sick leave and flexible vacation.
• Company-sponsored health, vision, and dental insurance - including spouse/dependents (🇺🇸 only).

Our Principles

Autonomy & Independence

• Don’t ask for permission - Gensyn fosters a constraint culture, not a permission culture.
• Claim ownership of work streams, setting goals and deadlines proactively.
• Push & pull context on your work rather than waiting for information.
• Communicate to be understood, ensuring clarity rather than pushing out information.
• Maintain a small team size to minimize misalignment and politics.

Rejection of Mediocrity & High Performance

• Give direct, immediate feedback to everyone.
• Embrace an extreme learning rate, continuously expanding knowledge.
• Don’t quit – push to achieve final outcomes despite barriers.
• Be anti-fragile, balancing short-term risk for long-term outcomes.
• Reject waste, guarding company time from unproductive meetings or bikeshedding.

Key skills/competency

• Applied Machine Learning
• Reinforcement Learning
• ML Systems Optimization
• Distributed Systems
• Fault-Tolerant ML
• Software Engineering Fundamentals
• Data Structures & Algorithms
• ML Architecture Prototyping
• Peer-to-peer Networks
• Decentralized Infrastructure