Software Engineer, Salesforce
Machine Learning Infrastructure, Slack
$148.5-313.7k
+ Employee stock purchasing program
World's leading CRM provider
Open for applications
World's leading CRM provider
1001+ employees
Open for applications
$148.5-313.7k
+ Employee stock purchasing program
1001+ employees
Company mission
To empower companies to connect with their customers in a whole new way.
Role
Who you are
- Significant professional experience in software engineering with a strong focus on infrastructure, backend systems, platform engineering, or MLOps
- Deep experience building and operating distributed systems, including expert level knowledge of Kubernetes and container based platforms
- Hands on experience with modern ML infrastructure and serving stacks such as Ray or KubeRay, vLLM, or similar training and inference orchestration frameworks
- Experience working with GPU infrastructure, including performance optimization and operational management at scale
- Strong experience with data infrastructure and orchestration technologies such as Airflow, Spark, or similar systems
- Experience building and operating cloud native systems on public cloud platforms such as AWS, GCP, or Azure, including infrastructure as code
- A demonstrated ability to drive technical direction for complex systems and balance short term delivery with long term architectural goals
- Excellent written communication, as well as ability to thrive in an asynchronous and globally distributed infrastructure team
- A related technical degree required
What the job involves
- The AI and ML Infrastructure team is part of Slack’s Core Infrastructure organization and is responsible for the foundational systems that enable machine learning and AI across the company. The team designs, builds, and operates reliable, scalable, and high performance platforms that allow product and ML teams to develop, deploy, and operate AI driven capabilities with confidence
- The team owns shared infrastructure, services, and tooling that support the full ML lifecycle, including model training, deployment, inference, and monitoring. As Slack AI continues to grow, the team is evolving from traditional ML deployments toward large scale, highly distributed systems
- This work involves deep architectural decisions around scalable model deployment strategies, real time feature serving at very high throughput, GPU accelerated inference at message scale, and responsible training of models on sensitive data with strong privacy and safety requirements
- The ML Infrastructure focus area is responsible for the low level systems that power training and inference at scale. This includes architecting and maintaining distributed systems for model training, serving, and deployment using Kubernetes based platforms, GPU infrastructure, and open source ML stacks such as KubeRay and vLLM
- The team delivers platform capabilities that improve the speed, reliability, and quality of ML development, including training pipelines, feature generation systems, and compute orchestration
- We are looking for Software Engineers to join the ML Infrastructure focus area and help architect and operate the core systems that power AI at Slack. In this role, you will own foundational infrastructure for large scale model training and inference, and evolve it into a reliable, secure, and self service platform used across the company
- You will work at the intersection of distributed systems, GPU infrastructure, and modern ML stacks, solving complex scalability and reliability challenges. This role blends deep systems engineering with a strong understanding of the ML lifecycle, and plays a critical part in shaping the long term technical foundations of Slack’s AI capabilities
- Design, build, and operate systems to train, serve, and deploy machine learning models at scale, with a focus on reliability, performance, and operational simplicity
- Evolve GPU backed inference infrastructure to support high throughput, latency sensitive workloads, including large scale model serving
- Architect and optimize distributed training and data processing systems using platforms such as Ray, Airflow, Spark, or similar technologies
- Build and maintain Kubernetes based platforms and orchestration layers using tools such as KubeRay, vLLM, and internally developed services
- Architect solutions that bridge legacy systems with modern technologies while maintaining monolithic application stability
- Develop robust monitoring, observability, and alerting for production ML workloads to ensure operational excellence
- Partner closely with AI Platform, ML modeling, security, and product engineering teams to design infrastructure that supports evolving AI use cases
- Provide technical leadership through design reviews, mentorship, and by setting engineering standards and long term architectural direction for ML infrastructure
- Author technical design and architecture documentation, and contribute thought leadership through engineering blog posts
Our take
Salesforce is a global cloud computing company, best known for its on-demand, comprehensive customer relationship management (CRM) solutions. The company was launched to mitigate the doubts that business owners felt regarding third party companies holding their data.
By providing essential, customer-oriented CRM software, Salesforce places itself at the heart of digitised businesses. Furthermore, by building out its AI capabilities, the company removes the complexity of AI, enabling any business to deliver smarter, personalised and more predictive customer experiences.
Due to the continued innovation offered by the company, over 150,000 companies, both big and small, are growing their business with Salesforce today. Despite a tumultuous 2023 filled with activist investors, a boardroom shake-up and the unprecedented step-down of Co-CEO Bret Taylor, Salesforce continues to close several acquisition deals and managed to drive up its stock value by 96% from the end of 2022 to 2023.
Steph
Company Specialist at Welcome to the Jungle
Insights
-8% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Jun 2001
$46.9m
SERIES D
Nov 1999
$13.2m
SERIES C
Total funding: $64.5m
Company benefits
- With 9 standard holidays and four floating holidays, you get a total 13 paid days off each year
Company HQ
SoMa, San Francisco, CA
Leadership
Marc Benioff
(Chair & CEO)Previously Senior Vice President at Oracle Corporation for 13 years.
Parker Harris
(CTO)Previously worked as a Software Engineer for over six years. Harris also co-founded Left Coast Software.
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