Machine Learning Engineer

4 weeks ago

San Francisco, United States Salesforce, Inc. Full time

As a Machine Learning Engineer you will craft and implement ML and generative AI powered features that leverage our data to make a fabulous, robust, safe, and valuable product for our users. Our team has built out robust functionality spanning LLM deployment, evaluation, monitoring and quality improvements. We are looking for an expert engineer who has worked in the development of both traditional ML and more recent generative AI solutions to help guide the architecture and development of AI.


What you will do:
  • Brainstorm with Product Managers, Designers and 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.
  • Help other engineers actively own features or systems and define their long-term health, while also improving the health of surrounding systems.
  • Collaborate with peers across Engineering to triage bugs and tackle sophisticated production issues across the stack.
  • Mentor other engineers and deeply review code.
  • Improve engineering standards, tooling, and processes.
  • Design and deliver scalable RAG services that can be integrated with numerous applications, support thousands of tenants, and operate at scale in production.
  • Drive system efficiencies through automation, including capacity-planning, configuration management, performance tuning, monitoring, and root cause analysis.
  • Participate in periodic on-call rotations and be available to resolve critical issues.
  • Collaborate with Product Managers, Application Architects, Data Scientists, and Deep Learning Researchers to understand customer requirements, design prototypes, and bring innovative technologies to production.

You may be a fit for this role if you have:
  • 8+ years experience with machine learning and software engineering.
  • Put machine learning models, generative AI or other data-derived artifacts into production at scale, especially for text-based applications.
  • Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java.
  • Built with common ML frameworks like pytorch, Keras, XGBoost, Tensorflow or Scikit-learn.
  • An analytical and data driven approach, and know how to measure success with complicated ML/AI products.
  • Familiarity with search technologies like Elasticsearch and Solr.
  • Led technical architecture discussions and helped drive technical decisions within the team.
  • Write understandable, testable code with an eye towards maintainability.
  • Strong communication skills and the ability to explain sophisticated 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 equivalent training, fellowship, or work experience.
  • Proficient in Python, SQL, and libraries such as TensorFlow, PyTorch, or scikit-learn, with experience in handling large datasets and distributed computing frameworks (e.g., Spark, Kubernetes).
  • Highly skilled with statistical, AI/ML, and agentic techniques to understand behaviors of large scale distributed systems, cloud operations, deriving strategic insights from vast datasets. Hands-on experience with cloud platforms.
  • Experience with infrastructure automation tools (e.g., Terraform, CloudFormation) and an understanding of DevOps principles in the context of deploying AI/ML models.
  • Experience using telemetry and metrics to drive operational excellence.
  • Ability to communicate findings to executives and multi-functional product teams by translating scientifically rigorous analyses towards business impact.
  • Ability to learn quickly and deliver high-quality code in a fast-paced, dynamic team environment.
  • A meticulous and well versed authority in the area of MLOps including DataOps, ModelOps, CodeOps and Explainability.
  • Familiar with Agile development methodology and committed to continual improvement of team performance.
  • Effective communication, strong leadership skills, standout colleague who is capable of mentoring and being mentored by others.
  • Inventive and creative; on task and able to deliver incrementally and on time.

Preferred Skills:
  • Deployed production RAG pipelines.
  • Experience in A/B testing and experimentation.
  • Experience with LLM evaluation and monitoring at scale.
  • Experience with search or other ranking oriented ML features and systems, e.g. recommendations or ads ML.
  • Strong background in a wide range of ML approaches, from Artificial Neural Networks to Bayesian methods.
  • Experience with conversational AI.
  • Expertise in retrieval systems and search algorithms.
  • Familiarity with vector databases and embeddings.
  • Knowledge of demonstrating multiple data types in RAG solutions including structured, unstructured, and graph.
  • Worked on generative AI apps with Large Language Models and possibly fine tuned them or improved quality through other methods.
  • Experience building batch data processing pipelines with tools like Apache Spark, SQL, Hadoop, EMR, Airflow, Dagster, or Luigi.
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