Software Engineer II, Machine Learning (Search) - Slack
Salesforce · Montreal, QC
- On site
- Full-time
- $150,000 / year
- Montreal, QC
Job highlights
- Develop ML models for search and AI.
- Build data pipelines and fine-tune LLMs.
- Collaborate on new product features.
- Deploy ML models into production.
- Mentor engineers and improve processes.
About the role
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. 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, driving innovation, and keeping Salesforce's core values at the heart of it all. 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.About the Role
Slack is looking for a Machine Learning Engineer to craft and implement features, services, API methods, and models to leverage our data to make Slack a fabulous, robust, safe, and valuable product for our users. We're looking for candidates with experience or interest in search / conversational agents, but ultimately are looking for engineers who can help drive impact with machine learning across the organization.Impact at Slack
We have over 10 million daily active users relying on our product. At peak usage, a million messages a minute pass through Slack. During the week, our users spend over a billion minutes a day active in our product. Machine learning engineers at Slack touch a great variety of parts of our technical stack. At different points, you might find yourself building data pipelines, training search ranking models, fine-tuning LLMs, implementing features in our application, or analyzing experiment data. We don’t expect everyone to be an expert in everything, but we are looking for candidates with experience in Machine Learning, a strength in at least a couple of these, and who are excited to learn the rest. This is a practical machine learning team, not a research team. Our goal is to deliver business value with machine learning and data in whatever form that takes. Sometimes that means bootstrapping something simple like a logistic regression and moving on. Other times that means developing sophisticated, finely tuned models and novel solutions to Slack’s unique problem space. We are looking for engineers who are driven by driving impact for our business, building great products for our customers, and delivering robust, reliable services with machine learning.What You Will Be Doing
- Develop ML models supporting ranking, retrieval, and generative AI use-cases.
- Brainstorm with Product Managers, Designers and Frontend Engineers to conceptualize and build new features for our large (and growing!) user base.
- Produce high-quality results by leading or contributing heavily to large multi-functional projects that have a significant impact on the business.
- Actively own features or systems and define their long-term health, while also improving the health of surrounding systems.
- Support in the development of sustainable data collection pipelines and management of ML features.
- Assist our skilled support team and operations team in triaging and resolving production issues.
- Mentor other engineers and deeply review code.
- Improve engineering standards, tooling, and processes.
What You Should Have
- Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java.
- Built with common ML frameworks like PyTorch, TensorFlow, Keras, XGBoost, or Scikit-learn
- Experience building batch data processing pipelines with tools like Apache Spark, Hadoop, EMR, Map Reduce, Airflow, Dagster, or Luigi.
- Worked on generative AI apps with Large Language Models and possibly fine-tuned them.
- An analytical and data driven mindset, and know how to measure success with complicated ML/AI products.
- Put machine learning models or other data-derived artifacts into production at scale.
- Led technical architecture discussions and helped drive technical decisions within the team.
- The ability to write understandable, testable code with an eye towards maintainability.
- Strong communication skills and you are capable of explaining complex technical concepts to designers, support, and other specialists.
- Strong computer science fundamentals: data structures, algorithms, programming languages, distributed systems, and information retrieval.
- A bachelor's degree in Computer Science, Engineering, Statistics, Mathematics or a related field, or you have equivalent training, fellowship, or work experience.
Nice To Have
- Experience building and optimizing RAG pipelines.
- Expertise in conversational agents.
- Expertise in retrieval systems and search algorithms.
- Familiarity with vector databases and embeddings.
- Broad experience across NLP, ML, and Generative AI capabilities.
Key skills/competency
- Machine Learning
- Python
- TensorFlow
- PyTorch
- Generative AI
- LLMs
- Search Ranking
- Data Pipelines
- Information Retrieval
- Computer Science Fundamentals
Skills & topics
- Machine Learning Engineer
- ML Engineer
- AI
- Generative AI
- LLM
- Search
- Ranking
- Retrieval
- Python
- TensorFlow
- PyTorch
- Spark
- Salesforce
- Slack
How to get hired
- Tailor your resume: Highlight experience with ML frameworks, data pipelines, and programming languages mentioned in the job description.
- Showcase impact: Quantify your achievements in previous roles, especially in deploying ML models and driving business value.
- Prepare for technical interviews: Brush up on data structures, algorithms, information retrieval, and ML concepts.
- Demonstrate communication skills: Be ready to explain complex technical ideas clearly to non-technical stakeholders.
- Research Salesforce values: Understand their commitment to innovation, customer success, and AI integration.
Technical preparation
Master Python, ML frameworks, and data pipelines.,Practice algorithms and data structures.,Study information retrieval and search concepts.,Prepare to discuss LLM and generative AI experience.
Behavioral questions
Describe a complex ML problem you solved.,How do you collaborate with product managers?,Explain a technical concept to a non-expert.,How do you handle production issues and mentorship?
Frequently asked questions
- What are the key responsibilities for a Machine Learning Engineer at Slack?
- As a Machine Learning Engineer at Slack, you will develop ML models for ranking, retrieval, and generative AI use-cases. You will also brainstorm new features with cross-functional teams, build and manage data pipelines, deploy ML models into production, and mentor other engineers. The focus is on practical application and driving business value through machine learning.
- What technical skills are most important for this Machine Learning Engineer role at Salesforce (Slack)?
- Key technical skills include experience with programming languages like Python, ML frameworks such as TensorFlow and PyTorch, and building data processing pipelines with tools like Apache Spark. Familiarity with generative AI, LLMs, and deploying models into production is also crucial. Strong computer science fundamentals are essential.
- What is the impact of a Machine Learning Engineer at Slack?
- Machine Learning Engineers at Slack have a significant impact due to the product's large user base (over 10 million daily active users). Your work can influence how users interact with Slack by improving search, personalization, and the overall product experience. You'll be part of a team driving business value through practical AI applications.
- Does Salesforce use AI in its hiring process for this Machine Learning Engineer role?
- Yes, Salesforce utilizes AI tools to assist recruiters in assessing resumes and qualifications. However, human recruiters always make the final hiring decisions. You can find more information about their data usage and AI tools in their Candidate Privacy Statement.
- What kind of projects can I expect as a Machine Learning Engineer at Slack?
- You can expect to work on a variety of projects, including training search ranking models, fine-tuning LLMs, building data pipelines, implementing features, and analyzing experiment data. The role is hands-on, with a focus on delivering business value through sophisticated ML models and novel solutions.
- What is the difference between this role and a research-focused ML position?
- This role is a practical machine learning position focused on delivering business value and building robust products. While research is valued, the primary goal is implementation and impact, whether through simple models like logistic regression or sophisticated, finely-tuned solutions for Slack's specific challenges.