- Company Name
- Slack
- Job Title
- Software Engineer, Machine Learning (Senior, SWE II & SWE I)
- Job Description
-
Job Title: Software Engineer, Machine Learning (Senior, SWE II & SWE I)
Role Summary:
Drive the development and production of machine‑learning solutions for large‑scale conversational and AI products. Focus on enhancing chat‑bot interfaces, search, recommendation, and generative AI features, ensuring robust, scalable, and user‑centric delivery.
Expectations:
Senior expertise in full product lifecycle – from data pipeline design, model training, to deployment and monitoring. Proven experience leading technical architecture and influencing product strategy. Strong cross‑functional collaboration with PMs, designers, and front‑end teams. Demonstrated ability to mentor and uplift a technical team.
Key Responsibilities:
- Design, implement, and scale ML models for ranking, retrieval, and generative tasks.
- Build and maintain batch data processing pipelines (Spark, Hadoop, Airflow, etc.).
- Fine‑tune language models (LLMs, BERT) and embed them into production services.
- Own feature lifecycles; define long‑term health and performance metrics.
- Resolve production issues, triage incidents, and improve system reliability.
- Mentor junior engineers and conduct thorough code reviews.
- Enhance engineering standards, tooling, and processes.
Required Skills:
- Proficient in functional or imperative languages (Python, Go, Java, Scala, Ruby, PHP, C).
- Hands‑on with ML frameworks: PyTorch, TensorFlow, Keras, XGBoost, Scikit‑learn.
- Experience with batch pipeline tools: Apache Spark, Hadoop, EMR, Airflow, Dagster, Luigi.
- Strong data‑driven mindset; ability to measure model success and monitor performance.
- Deployment of ML models at scale; knowledge of production monitoring and observability.
- Solid software engineering practices – testable, maintainable code, strong communication skills.
Bonus Skills:
- Conversational agentic systems, retrieval/search algorithms.
- Vector databases, embeddings, RAG solutions, structured/unstructured data, knowledge graphs.
- Broad exposure to NLP, generative AI, and related ML capabilities.
Required Education & Certifications:
Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. Relevant certifications (e.g., AWS Certified Machine Learning, GCP ML Engineer) are a plus.