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About This Role
Role Type: External Research Affiliate / Think Tank Contributor
Location: South Florida preferred
Affiliation: Local university-based Masters or PhD researcher or postdoctoral AI researcher working under faculty direction
Organization: Brightstar.AI
Role Summary
Brightstar.AI is seeking a full-time or recently graduated Masters or PhD-level Applied AI Engineer Intern – Advancing Agent Quality with a strong research mindset, affiliated with a leading (South Florida) university, to contribute to its AI Think Tank and to the hands-on build-out of Brightstar’s proprietary AI systems. The intern will pair academic rigor with applied engineering: building the evaluation environments, automated raters, and diagnostic tooling that establish — with evidence rather than impression — whether an agent is reliable enough to put in front of operators, executives, and investors.
The first concrete assignment is the AI Twin Board, Brightstar’s proprietary board-simulation product, where the intern will help build the evaluation harness end to end: realistic task suites, calibrated LLM judge, failure-mode diagnostics, and the data flywheel that feeds improvements back into the agents themselves. The AI Twin Board is the starting point, not the full scope — as Brightstar’s AI portfolio expands, the same quality discipline will be applied to other agentic systems, internal platforms, and portfolio-company deployments.
Working in coordination with university professors and research leaders, the intern will also bring up-to-date knowledge of the fast-evolving AI landscape — especially large language models, reasoning systems, emerging model capabilities, and the societal implications of advanced AI — and help translate what current and next-generation systems may enable over the next six months to three years into Brightstar’s evaluation standards and Think Tank point of view.
Key Responsibilities
- Design, build, and scale realistic agent environments and task suites that reflect how Brightstar’s agents are actually used — board-level deliberation, research synthesis, diligence workflows, and multi-step tool use.
- Develop agentic LLM judge, calibrate them against human expert evaluation, and benchmark Brightstar agents against leading frontier models.
- Build trajectory-analysis frameworks and diagnostic tooling that root-cause agent failure modes — passivity, hallucination, persona drift, brittle tool execution — and feed fixes back into agent prompts, harnesses, and architecture.
- Enable agents to generate and iterate on their own verifiers (automated test cases, checklists, ground-truth sets) to support effective exploration and iterative problem-solving.
- Harvest multi-turn interaction trajectories into high-quality datasets and reward signals that support fine-tuning and post-training of the models Brightstar relies on.
- Contribute to the AI Twin Board build-out as the initial focus, and extend the same evaluation approach to other Brightstar AI initiatives as the roadmap grows.
- Contribute frontier AI research perspectives to the Brightstar.AI Think Tank as part of weekly review sessions and select strategic discussions.
- Review relevant research papers, conference findings, and publications from leading AI institutions and labs as inputs to the Think Tank knowledge base and to Brightstar’s evaluation methodology.
- Help interpret academic and technical developments for a business and investment audience, including implications for industry transformation and applied AI opportunities.
Minimum Qualifications
- Currently enrolled in a Masters or PhD program — or serving as a postdoctoral or early-career academic researcher — in Artificial Intelligence, Machine Learning, Computer Science, Computational Linguistics, or a related field.
- 1 years of research, project, or professional experience with LLMs or Agents.
- 2 year of experience with machine learning and deep learning.
- Experience in software engineering and cloud-based development.
- Experience with agent builders, e.g. OpenSDK, OpenAI Agent Builder, etc.
- Able to communicate complex technical concepts clearly to non-technical executive audiences.
Preferred Qualifications
- In-depth knowledge of machine learning algorithms, including supervised learning and reinforcement learning.
- Hands-on experience building LLM or agent evaluation systems — autoraters, human-evaluation pipelines, benchmark design, or evaluation infrastructure.
- Experience with multi-agent orchestration, retrieval-augmented generation, and tool-use / function-calling reliability.
- Active research or publications in LLMs, reasoning systems, model behavior, AI safety, or the societal impact of advanced AI systems.
- Strong research discipline, intellectual curiosity, and the ability to separate meaningful signal from AI hype.
- Affiliated with a (South Florida) university and working under the supervision of a faculty member or professor, or recent graduate.
Why This Role Matters
Brightstar.AI’s Think Tank is intended to provide systematic forward-looking intelligence on where AI may create value, where it may disrupt industries, and how firms can better anticipate what may work tomorrow rather than only what worked yesterday. Masters and PhD-level Applied AI Engineers are a critical part of that model, bringing academic depth, frontier perspective, and analytical rigor into the Think Tank’s quarterly insights and longer-term point of view. Moreover, this role extends beyond research to focus on the practical application of AI in terms of AI Twin Board technology, which we see as a key differentiator in our research approach and value creation methodology.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Brightstar, 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 $212,000 based on 11,857 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Brightstar AI Hiring
Brightstar has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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