Interested in this AI/ML Engineer role at BigBear.ai?
Apply Now →Skills & Technologies
About This Role
Overview:
BigBear.ai is seeking a AI Innovation Intern to support BigBear.ai’s Innovation Org. This internship is designed for a student pursuing a degree in Information Systems, Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related technical field.
The Innovation Org serves as a connective layer across BigBear.ai’s business units and corporate functions, helping teams adopt GenAI tools, identify reusable capabilities, standardize effective workflows, and translate emerging AI technologies into practical enterprise value. The intern will work alongside senior technical leaders to help build agentic workflows, support AI enablement activities, research emerging tools and patterns, document reusable practices, and assist with mapping business\-unit needs to technical capabilities.
This role is well\-suited for a student who enjoys both hands\-on technical development and cross\-functional problem solving. The ideal candidate is comfortable learning quickly, asking good questions, organizing ambiguous information, and turning prototypes, research, or team feedback into useful artifacts for broader enterprise adoption.
What you will do:
- Assist in the design, development, and testing of AI\-enabled workflows, agentic prototypes, RAG pipelines, and reusable automation patterns
- Support the Innovation Org in identifying common technical needs, workflow gaps, and reusable capabilities across business units and corporate functions
- Help research, evaluate, and document emerging GenAI tools, agent frameworks, vector databases, retrieval patterns, prompt workflows, and software\-development practices
- Contribute to lightweight prototypes, demos, internal tools, or reference implementations that demonstrate practical use of GenAI and automation
- Assist with mapping business\-unit needs to existing BigBear.ai capabilities, reusable assets, internal expertise, and potential technology solutions
- Support documentation of reusable engineering assets, prompts, workflows, reference architectures, lessons learned, and internal enablement materials
- Collaborate with senior engineers and technical leads on software\-development tasks using Python, APIs, databases, cloud tools, and modern AI/ML libraries
- Help prepare internal artifacts such as capability maps, technical summaries, use\-case notes, workflow diagrams, demo materials, and research briefs
- Participate in agile development activities, code reviews, technical discussions, and knowledge\-sharing sessions
- Support enterprise AI adoption efforts by helping capture lessons learned, success stories, technical patterns, and opportunities for standardization
What you need to have:
- Currently enrolled in a Bachelor's or Master's degree program in: Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical field
- U.S. Citizenship required
- Familiarity with at least one programming language such as Python, JavaScript, or other Object Oriented or Functional programming languages
- Basic understanding of AI, machine learning, data science, or large language model concepts
- Interest in GenAI tools, agentic workflows, retrieval\-augmented generation, automation, or applied AI systems
- Ability to work with structured and unstructured information and synthesize findings into clear technical notes or recommendations
- Strong problem\-solving, analytical, and communication skills
- Ability to work in a remote or hybrid environment with guidance from senior technical staff
- Willingness to learn new tools, frameworks, and enterprise workflows quickly
What we'd like you to have:
- Experience with Python and common data/AI libraries such as pandas, Polars, NumPy, scikit\-learn, or similar tools
- Exposure to LLM\-based workflows, RAG systems, LangChain, Hugging Face, vector databases, embeddings, or prompt engineering
- Familiarity with relational databases, SQL, PostgreSQL, or database\-backed application development
- Exposure to vector databases such as Weaviate, Pinecone, Chroma, FAISS, or similar technologies
- Experience with Docker, GitHub, CI/CD concepts, cloud platforms, or DevOps practices
- Familiarity with REST APIs, Flask, FastAPI, web applications, or full\-stack development concepts
- Experience building or contributing to prototypes, internal tools, dashboards, notebooks, or user\-facing technical workflows
- Exposure to knowledge graphs, graph databases, semantic search, or entity\-relationship modeling
- Ability to document technical patterns, summarize research findings, and create reusable materials for broader team adoption
- Prior internship, academic project, or team project experience involving AI/ML, data engineering, software development, or automation
About BigBear.ai:
BigBear.ai is a leading provider of AI\-powered decision intelligence solutions for national security, supply chain management, and digital identity. Customers and partners rely on Bigbear.ai’s predictive analytics capabilities in highly complex, distributed, mission\-based operating environments. Headquartered in McLean, Virginia, BigBear.ai is a public company traded on the NYSE under the symbol BBAI. For more information, visit https://bigbear.ai/ and follow BigBear.ai on LinkedIn: @BigBear.ai and X: @BigBearai.
BigBear.ai is an Equal opportunity employer all protected groups, including protected veterans and individuals with disabilities.
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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BigBear.ai, 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 $180,000 based on 12,397 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,760.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
BigBear.ai AI Hiring
BigBear.ai has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span US, Annapolis Junction, MD, US, Columbia, MD, US.
Location Context
AI roles in Austin pay a median of $218,800 across 509 tracked positions. That's 9% above the national 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.