AI Infrastructure Engineer
Intercom · Dublin +1 · 5mo ago
Fin, now part of Salesforce, is on a mission to help businesses provide perfect customer experiences.
Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk, giving modern support teams one single system.
Together with Salesforce, the #1 AI CRM, where humans with agents drive customer success, we're building the future of customer experience. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword, it's a way of life. The world of work as we know it is changing, and we're looking for Trailblazers who are passionate about bettering business and the world through AI.
Ready to level up your career at the company leading workforce transformation in the agentic era? You're in the right place. Agentforce is the future of AI, and you are the future of Salesforce.
What's the opportunity?
We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Fin's next generation of AI products.
Fin is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month.
You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks, and is the foundation of the AI Group's full stack approach to AI.
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.
- Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly.
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 Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
Policies
Fin has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week.
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