About Benzinga
Benzinga is a fast-growing financial media and data technology company reshaping how investors access information. We combine artificial intelligence, machine learning, and real-time data pipelines to surface insights before they hit the mainstream. Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading banks, fintechs, and AI companies worldwide.
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.
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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.
Requirement for applying:
- During your screening you will be required to submit a Loom video walkthrough of your most exceptional product, share relevant code/repo links, and describe the biggest challenge you faced building it.
Required Qualification
- 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)
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Why Join Benzinga
- 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.