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About This Role
Overview:
In today’s rapidly evolving technology landscape, an organization’s data has never been a more important aspect in achieving mission and business goals. Our AI \& Data Exploitation experts work with our clients to support their mission and business goals by creating and executing a comprehensive data strategy using the best technology and techniques, given the challenge.
At Steampunk, our goal is to build and execute a data strategy for our clients to coordinate data collection and generation, to align the organization and its data assets in support of the mission, and ultimately to realize mission goals with the strongest effectiveness possible.
For our clients, data is a strategic asset. They are looking to become a facts\-based, data\-driven, customer\-focused organization. To help realize this goal, they are leveraging visual analytics platforms to analyze, visualize, and share information. At Steampunk you will design and develop solutions to high\-impact, complex data problems, working with the best and data practitioners around. Our data exploitation approach is tightly integrated with Human\-Centered Design and DevSecOps.
Contributions:
We are looking for a skilled AI Developer to design, build, and optimize advanced AI solutions across predictive, generative, and autonomous system domains. This role requires solid hands\-on engineering capabilities, familiarity with modern AI architectures, and the ability to translate mission needs into robust, production\-ready AI capabilities. The AI Developer will work across the full stack of AI development, from data ingestion and model experimentation to application integration, orchestration, and deployment, and will collaborate closely with product teams, LLMOps engineers, designers, and mission stakeholders.
- Develop end\-to\-end AI solutions including LLM\-powered applications, predictive ML models, multi\-agent workflows, RAG pipelines, and specialized AI microservices.
- Implement reusable AI components, libraries, and APIs that streamline application development and accelerate delivery across programs.
- Integrate AI models with enterprise systems, APIs, data platforms, vector databases, and cloud\-native services to deliver scalable mission capabilities.
- Drive iterative experimentation, prototyping, and model improvement cycles in collaboration with Data Scientists and AI Evaluation Scientists.
- Design and implement advanced prompt strategies, context management layers, retrieval systems, and LLM orchestration logic.
- Build scalable inference services, optimize model performance, and collaborate with LLMOps to enable robust deployment, monitoring, and continuous improvement.
- Translate user needs and mission workflows into intuitive, reliable AI\-powered features through active partnership with designers and product teams.
- Implement secure\-by\-design and trustworthy AI practices, including safety guardrails, input sanitization, content filtering, and integration of evaluation metrics.
- Contribute to internal AI frameworks, code patterns, and shared accelerators that raise delivery quality across the AI \& Data Exploitation Practice.
- Participate in code reviews and support engineering excellence across multi\-disciplinary AI delivery teams.
- Stay current with emerging AI techniques, libraries, foundation models, and agent frameworks, evaluating their applicability to client missions.
- You will contribute to the growth of our AI \& Data Exploitation Practice!
Qualifications:
- Ability to hold a position of public trust with the U.S. government.
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
- 2\+ years of hands\-on software engineering experience, with exposure to AI/ML, generative AI, or LLM\-driven application development.
- Strong proficiency in Python and modern AI frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex, or similar.
- Demonstrated ability to design and develop production\-grade AI applications, including APIs, back\-end services, orchestration logic, and front\-end integrations (when needed).
- Experience implementing RAG architectures, embeddings, vector stores, and context retrieval patterns.
- Familiarity with multi\-agent orchestration frameworks, prompt engineering strategies, and advanced LLM interaction design.
- Strong understanding of cloud platforms (AWS, Azure, GCP), including compute, serverless services, and security fundamentals for AI workloads.
- Working knowledge of containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for AI\-based systems.
- Experience with structured and unstructured data, document processing, and application integration with existing enterprise systems.
- Understanding of responsible AI principles including safety, fairness, privacy, and model risk mitigation.
- Strong analytical and communication skills with the ability to collaborate across engineering, design, and mission domains.
- A collaborative mindset and a commitment to raising the technical quality of development work.
About steampunk:
Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $145,000\. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk’s total compensation package for employees. Learn more about additional Steampunk benefits here.
Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human\-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. As an employee owned company, we focus on investing in our employees to enable them to do the greatest work of their careers – and rewarding them for outstanding contributions to our growth. If you want to learn more about our story, visit http://www.steampunk.com. *We are an equal opportunity* *employer* *and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Steampunk* *participates* *in the E\-Verify program.*
Salary Context
This $115K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Steampunk, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($130K) sits 30% below the category median. Disclosed range: $115K to $145K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Steampunk AI Hiring
Steampunk has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in McLean, VA, US. Compensation range: $145K - $190K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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