Application Guide
How to Apply for Senior Research Engineer
at FAR AI
🏢 About FAR AI
FAR AI uniquely operates at the intersection of academia and industry, incubating high-impact AI safety research that's too resource-intensive for universities but not yet commercially viable. They focus specifically on ensuring AI systems are trustworthy and beneficial to society, offering the opportunity to work on foundational safety problems with real-world implications. Their mission-driven approach and focus on scaling research make them appealing for engineers who want their technical work to directly contribute to AI alignment and safety.
About This Role
As a Senior Research Engineer at FAR AI, you'll be accelerating and scaling their core safety research agendas by tackling challenging engineering bottlenecks that enable larger-scale experiments. You'll mentor and unblock other researchers on technical implementation while focusing on either ML infrastructure (transformers, PyTorch/JAX) or high-performance computing (Kubernetes, distributed systems, GPU optimization). This role directly impacts how quickly FAR AI can advance critical AI safety research by improving research velocity and technical depth.
💡 A Day in the Life
A typical day might involve reviewing performance bottlenecks in a transformer training pipeline, pairing with a researcher to debug distributed training issues on their Kubernetes cluster, and designing improvements to enable larger-scale safety experiments. You'd split time between hands-on engineering (optimizing code, configuring cluster resources) and collaborative problem-solving with research staff to accelerate their work.
🚀 Application Tools
🎯 Who FAR AI Is Looking For
- Has 5+ years of software engineering experience demonstrated through production systems or substantial open-source contributions in Python
- Either: (1) Extensive experience training transformers with PyTorch/JAX and strong ML fundamentals (linear algebra, statistics), OR (2) Deep expertise in Kubernetes/SLURM cluster orchestration and building high-performance distributed systems for ML workloads
- Proven track record of mentoring other engineers or scientists on technical implementation and engineering best practices
- Motivated by research impact over pure engineering elegance, with interest in AI safety problems and scaling research capabilities
📝 Tips for Applying to FAR AI
Highlight specific examples where you've 'unblocked' other researchers or engineers on technical challenges, especially in ML or distributed systems contexts
If applying under Option 1 (ML), detail your experience with transformer training at scale; if Option 2 (HPC), emphasize your Kubernetes/SLURM expertise and GPU optimization work
Demonstrate your understanding of FAR AI's research philosophy by referencing specific projects or papers from their website that align with your technical background
Include links to open-source contributions that showcase both your Python fluency and either ML or HPC expertise relevant to their needs
Quantify your impact on research velocity in previous roles - how your engineering work enabled faster experimentation or larger-scale studies
✉️ What to Emphasize in Your Cover Letter
["Explain why FAR AI's specific mission (bridging academia-industry gaps in AI safety) resonates with you personally and professionally", "Detail which core competency (ML or HPC) you're applying under and provide concrete examples of relevant experience", "Describe your mentoring philosophy and how you've helped researchers overcome technical barriers in past roles", "Connect your engineering background to scaling research - how you've enabled larger experiments or more complex studies through technical work"]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Study FAR AI's research publications and ongoing projects on their website to understand their technical approaches to AI safety
- → Research their team structure and identify which research agendas align with your ML or HPC expertise
- → Understand their philosophy about 'research too resource-intensive for academia but not ready for commercialization' and think about how you'd contribute
- → Look into their technical stack mentions (PyTorch/JAX, Kubernetes) and prepare to discuss your experience with these specific tools
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on engineering elegance without connecting to research impact or velocity improvement
- Being vague about which core competency (ML vs HPC) you're applying under or trying to cover both without depth in either
- Treating this as a generic ML engineering role without demonstrating specific interest in AI safety or FAR AI's mission
- Not providing concrete examples of mentoring experience or helping others overcome technical barriers
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!