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About This Role
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.
Required 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)
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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.
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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Benzinga, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Benzinga AI Hiring
Benzinga has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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