Offer summary

Qualifications:

Strong background in applied machine learning/engineering., Hands-on experience with distributed model training., Proven background in training, retraining, and monitoring machine learning systems and models., Impeccable analytical and problem-solving skills..

Key responsabilities:

Build scalable, distributed ML compute systems over decentralised infrastructure.

Design and develop novel machine learning algorithms and deep learning applications.

Partner with researchers and production engineers to run novel experiments.

Maintain highly fault-tolerant production systems.

Job description

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.

The Role

Gensyn is looking for a Machine Learning Engineer

Machine Learning Engineers at Gensyn design and implement highly decentralised training (and generation) pipelines. Their scope can span proof of concept development for novel ML research—e.g., our RL-Swarm reinforcement learning framework or Verde verification system—to the maintenance of highly fault-tolerant production systems.

Responsibilities

Build scalable, distributed ML compute systems over uniquely decentralised and heterogeneous infrastructure.
Design and develop novel machine learning algorithms and deep learning applications, and systems for Gensyn; likely from scratch or by augmenting existing systems.
Partner with both researchers and production engineers to design and run novel experiments, taking research from theory to production.

Competencies

Must have

Strong background in applied machine learning/engineering.
Hands-on experience with distributed model training.
Comfortable working in an experimental environment, with extremely high autonomy and unpredictable timelines
Proven background in training, retraining, and monitoring machine learning systems and models.
Impeccable analytical and problem-solving skills.
Familiarity with data structures and software architecture.

Preferred

Experience building highly performant, distributed systems.
Demonstrated background developing or implementing novel ML research.
Experience developing mission-critical, highly complex production systems, particularly with respect to improving their fault tolerance/crash-recovery.

Nice to have

Experience working in a startup/scaleup environment.
On-call experience during P0/P1-level incidents.

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]

Our Principles

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.
Build and design thinly.

Required profile