Join Baseten as a Software Engineer to architect and develop scalable infrastructure for ML training platforms.
Posted by employer 1 year ago
First seen on Joblaze 1 month ago
Last verified on the company career page 11 hours ago
Skills & Technologies
Requirements
- Education
- Bachelor's degree
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In the role of Software Engineer on the Training Infrastructure team, the individual will design and develop scalable systems that support machine learning training, focusing on aspects like scheduling and storage. Proficiency in Go and deep knowledge of Kubernetes are essential, along with experience in distributed systems and MLOps. This position is ideal for someone with a strong technical background and a passion for AI, particularly suited for mid to senior-level engineers. Baseten is rapidly growing, having recently secured significant funding, and offers a collaborative environment for innovation.
Joblaze insights
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI, AWS, Airflow, Axolotl, DeepSpeed, FSDP.
- What seniority level is this role?
- baseten targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Software Engineer - Training Infrastructure role at baseten.
From the original posting
THE ROLE
As a Software Engineer on the Training Infrastructure team, you'll architect and lead development of our training platform, supporting top tier research engineers and model developers. You'll make key technical decisions for the infrastructure enabling developers to deploy, scale, and monitor their workloads with high performance and reliability. You’ll own scheduling, storage, networking, reliability, and observability of technical systems in the training stack
EXAMPLE INITIATIVES
Take a look at what we’ve built so far:
RESPONSIBILITIES
Design and architect scalable infrastructure systems for our ML training platform (e.g. scheduling, storage, and networking)
Partner closely with developers and research engineers to translate complex training requirements into technical solutions
Design and architect a global training scheduler
Design and architect reinforcement learning systems and continuous learning pipelines
Drive long-term improvements to increase reliability of systems and velocity of development
Partner closely with SRE and Capacity teams to unlock state of the art training infrastructure
Make critical architectural decisions balancing performance with system reliability
Lead technical discussions and mentor junior engineers on infrastructure best practices
Contribute to long-term technical strategy and infrastructure roadmap
REQUIREMENTS
Bachelor’s degree or higher in Computer Science or related field
5+ years of experience
Proficiency in Go, with
Deep expertise with Kubernetes in production environments
Advanced understanding of distributed systems concepts and performance tuning
Proven experience designing observability systems
Experience with ML/AI workloads and MLOps platforms
NICE TO HAVE
Experience with distributed storage systems
Python experience a plus
Extensive experience with major cloud providers (AWS, GCP) and neo-cloud providers (Crusoe, DigitalOcean, Nebius)
Experience with workload orchestration platforms like Temporal or Airflow
Familiarity or experience with the open source training stack and frameworks (NCCL, PyTorch, Megatron, NemoRL, VeRL, Axolotl, HF Trainer) and distributed training techniques (FSDP, DeepSpeed).
Experience developing AI products, tooling, or agents
BENEFITS
Competitive compensation, including meaningful equity
Paid parental leave
Fertility and family-building stipend through Carrot
(U.S. only) Company-facilitated 401(k)
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