AI Infrastructure Engineer

We’re particularly interested in engineers who have

• A track record of working on model training or model inference at scale, or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better.

What will I be doing?

As a Senior AI Infrastructure Engineer focused on model training and inference, you will:

• Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation.

• Build and optimize inference services that deliver low‑latency, high‑reliability experiences for our customers, including autoscaling, routing, and fallbacks.

• Work on GPU‑level performance: tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack.

• Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production.

• Play an active role in hiring, mentoring, and developing other engineers on the team.

• Raise the bar for technical standards, reliability, and operational excellence across Fin's AI platform.

Profile we’re looking for

These are indicative, not hard requirements

We’re looking to hire Senior+ AI Infrastructure Engineers. You’re likely a great fit if:

• You have 5+ years of experience in software engineering, with a strong track record of shipping high‑quality products or platforms.

• You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals).

• You have hands‑on experience with one or more of the following:

• Model training (especially transformers and LLMs).

• Model inference at scale (again, especially transformers and LLMs).

• Low‑level GPU work, such as writing CUDA or Triton kernels.

• Comfortable working in production environments at meaningful scale (traffic, data, or organizational).

• You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers.

• You take pride in strong technical fundamentals, love learning, and are willing to invest in your own development.

Bonus skills & attributes

None of these are required, but they’re nice to have

• Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies).

• Experience running training or inference workloads on Kubernetes.

• Experience with AWS or other major cloud providers.

• Production experience with Python in ML or infrastructure contexts.

• Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings

#LI-Hybrid

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. At Fin, we want to give people what they need to do the best work of their careers. Our benefits and programs are designed to support your health and wellbeing, your family, your time away from work, and your financial future. Offerings vary by location in line with local practices and requirements. Learn more about working at Fin and the benefits we offer at fin.ai/careers. Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initi…