[Hiring] AI Machine Learning Engineer @Benzinga

Role Description

We're seeking a highly motivated AI / Machine Learning Engineer who thrives at the intersection of data science and backend engineering — someone who can take a model from notebook to production, and architect intelligent systems in Go and Python that scale to millions of requests. The ideal candidate is a self-starter who independently identifies opportunities, experiments with new approaches, and ships production-ready solutions without constant direction.

Key Responsibilities

• AI / Machine Learning
  • Research, design, and deploy machine learning models across NLP, time-series forecasting, and event detection domains.
  • Build LLM-driven systems (e.g. summarization, RAG pipelines, embedding search) optimized for financial news and quantitative data.
  • Develop model serving APIs and scalable inference layers using Go or Python.
  • Implement model monitoring, drift detection, and continuous retraining pipelines.
  • Work with financial text (earnings call transcripts, filings, news) to extract structured insights.
  • Collaborate with data engineers to build training datasets, feature stores, and embedding databases.
• Backend & Infrastructure
  • Develop and maintain high-performance Python or Go microservices that integrate with AI systems and Go data APIs.
  • Design and optimize real-time inference pipelines on AWS, leveraging ECS/EKS, S3, and Lambda.
  • Ensure low-latency, fault-tolerant, and scalable delivery of AI-powered data.
  • Implement CI/CD for ML workflows, including containerization, automated deployment, and versioning.
  • Partner with DevOps to manage cloud infrastructure and ensure robust observability for AI workloads.

Qualifications

• 4+ years of experience in AI/ML or data engineering roles, with a proven track record of deploying ML models in production.
• Computer science degree (Bachelor minimum).
• Deep proficiency in Python (data, ML) and Go (backend, microservices).
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Experience with transformer architectures, embeddings, or fine-tuning LLMs.
• Strong understanding of data pipelines, feature extraction, and model lifecycle management.
• Familiarity with Docker, Kubernetes, and AWS (EKS, S3, Lambda, EC2).
• Excellent problem-solving skills and ability to work independently in a distributed environment.

Preferred Skills / Experience

• Startup experience.
• Financial services or fintech background.
• Experience building LLM-powered APIs or retrieval-augmented generation (RAG) systems.
• Knowledge of vector databases (e.g., Pinecone, Weaviate, FAISS, OpenSearch kNN).
• Experience with Kafka, LangChain, or data streaming architectures.
• Familiarity with financial data systems, real-time analytics, or news NLP.
• Exposure to MLOps tools (MLflow, BentoML, SageMaker, Airflow, etc.).
• Contributions to open-source ML or Go projects are a strong plus.

Tech Stack

• Languages: Python, Go
• ML Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain
• Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
• Containers & Orchestration: Docker, Kubernetes
• Data & Streaming: Kafka, Postgres, OpenSearch
• CI/CD: GitHub Actions, GitLab CI
• Monitoring: Datadog, Prometheus, Grafana
• Version Control: Git (Gitlab / Github)

Benefits

• Build and ship production AI systems that shape how financial markets understand information.
• Operate with full creative freedom — explore, experiment, and execute your ideas end-to-end.
• Work with a lean, highly technical team where initiative and ownership are celebrated.
• Fully remote, high-trust environment that rewards curiosity, speed, and execution.

IMPORTANT

Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it.