Application Guide
How to Apply for Senior Machine Learning Infrastructure Engineer
at Plus
🏢 About Plus
Plus is at the forefront of autonomous driving technology, leveraging AI to create safer and more sustainable transportation. Their remote-first culture and focus on cutting-edge ML infrastructure make them an exciting place for engineers who want to impact real-world systems at scale.
About This Role
As a Senior ML Infrastructure Engineer at Plus, you'll build the backbone that enables autonomous driving models to be trained, deployed, and monitored reliably. Your work on GPU clusters, data pipelines, and distributed systems directly powers the AI that drives vehicles safely.
💡 A Day in the Life
You might start by reviewing cluster health dashboards and alerts, then join a stand-up to discuss pipeline bottlenecks. Later, you could design a new data ingestion system or troubleshoot a distributed training job, collaborating with ML researchers to optimize resource allocation.
🚀 Application Tools
🎯 Who Plus Is Looking For
- Experienced with large-scale GPU cluster management and distributed training (e.g., PyTorch DDP, Horovod, or similar).
- Proficient in Python and C++, with deep knowledge of containerization (Docker, Kubernetes) and orchestration for ML workloads.
- Skilled in building and maintaining data pipelines, model versioning (MLflow, DVC), and experiment tracking at scale.
- Comfortable with multi-cloud environments (AWS, GCP) and on-premise infrastructure, including monitoring and alerting systems (Prometheus, Grafana).
📝 Tips for Applying to Plus
Highlight specific projects where you designed or scaled ML infrastructure, including metrics like training throughput, cluster utilization, or latency improvements.
Tailor your resume to emphasize experience with autonomous driving or robotics, even if indirect, to show domain awareness.
Mention any contributions to open-source ML infrastructure tools (e.g., Kubeflow, Ray, or custom solutions) as a strong differentiator.
Include a brief note in your cover letter about how your work directly impacts safety and reliability, aligning with Plus's mission.
Prepare to discuss trade-offs in system design for real-time inference vs. batch training, as this is critical for autonomous driving.
✉️ What to Emphasize in Your Cover Letter
['Your experience designing scalable ML systems that handle massive data volumes and high-availability requirements.', 'Specific technical achievements with GPU clusters, distributed training, or cloud infrastructure that reduced costs or improved performance.', 'Understanding of the unique challenges in autonomous driving (e.g., latency, safety, data diversity) and how your skills address them.', "Passion for the mission of safer, greener transportation and how you want to contribute to Plus's impact."]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read about Plus's autonomous driving technology stack, including their sensor suite and perception systems, to understand infrastructure needs.
- → Look into Plus's published papers or blog posts on ML infrastructure or deployment challenges in autonomous driving.
- → Research their remote work culture and how they manage collaboration across time zones for infrastructure teams.
- → Check recent news about Plus's partnerships or deployments to understand the scale and real-world impact of their systems.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on model development rather than infrastructure; this role is about building the platform, not training models.
- Not providing concrete metrics or examples of scaling infrastructure; vague claims like 'improved performance' are insufficient.
- Ignoring the autonomous driving context; generic ML infrastructure experience without domain relevance may be seen as a gap.
📅 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!