Interested in this AI/ML Engineer role at Citian?
Apply Now →Skills & Technologies
About This Role
Citian is a fast growing, venture backed SaaS technology company based in Washington, DC. Our software solutions revolutionize how our transportation and infrastructure systems – roads, rail, transit, bicycle, pedestrian – operate.
Our tech solutions:
- Reduce traffic fatalities
- Enhance pedestrian accessibility
- Empower system operators to save time & money
You'll work with some of the brightest minds in the software and transportation industries. Our engineers and data scientists apply the latest in emerging tech, Artificial Intelligence, and Machine Learning to build smarter, more advanced tools for our diverse client base. We work with clients across the United States, with global ambitions in the years ahead.
Who You Are:
You're a product-minded software engineer who turns customer problems into reliable AI-enabled capabilities. You've built and shipped production LLM features, and you know the difference between a convincing demo and a dependable product. You work across the stack, prototype quickly, validate both the value and the correctness of what you build, and turn the experiments that work into production software. You know when AI is the right approach and when conventional software is better. You're comfortable with ambiguous requirements and work with engineering, data science and customer-facing teams to find where AI creates real value for transportation agencies.
Expected Duties:
- Work with product, design, Customer Success and data science to understand agency problems, and help find and validate where AI adds value and where it doesn't.
- Design, build and ship AI-enabled capabilities within our products, end to end, from data access to the application.
- Fit model-based features into our existing software and data architecture without building unnecessary AI infrastructure, and record the key design decisions.
- Design and maintain evaluations for AI features, including correctness, grounding, authorization, appropriate abstention and regression detection. Establish practical release criteria and use production feedback to improve quality.
- Make AI-generated results traceable to their underlying data, queries and assumptions.
- Enforce customer isolation and each user's own permissions, including when tools retrieve or query data, and guard against prompt injection.
- Own features in production: latency, cost, reliability, monitoring, failure modes and incident response.
- Choose the right technique for each problem (prompting, tool use, retrieval, text-to-SQL, fine-tuning or no AI at all), weighing accuracy, cost, speed and time to market.
Required Skills & Experience:
- Bachelor's degree in Computer Science, Data Science, or a related field
- 5+ years building production software, including shipped LLM features used by customers
- Demonstrated ability to take an ambiguous product problem through technical discovery, implementation, evaluation and production delivery
- Has designed and run evaluations for LLM output: test sets, correctness and grounding measures, regression gates, and catching wrong answers before customers do
- Full-stack product skills: Python or TypeScript back ends, SQL and relational databases (PostgreSQL), APIs, and enough front end to ship a feature end to end
- Production practices: code review, CI/CD, testing, monitoring and cost tracking
- Uses AI coding tools and agents effectively in daily engineering work, including implementation, testing and investigation, while independently validating their output
- Clear written English and solid overlap with US Eastern hours
Preferred Qualifications:
- Snowflake, AWS (including Bedrock), or LLMs over analytical or structured data
- Geospatial, transportation, public-sector or other complex, high-stakes data
- Security and compliance work around customer data (multi-tenant isolation, authorization, audit, SOC 2)
- Has led a feature end to end across teams
Your Citian Advantage:
- Competitive salary and benefits package including medical, dental, and vision insurance and generous PTO.
- 401(k) company match and monthly commuter transportation benefit
- On-site gym and free snacks in the office
- High-growth potential and opportunities for advancement within a fast growing, venture-backed SaaS startup!
Let's build smarter cities together! Learn more about Citian here: www.citian.co
** Citian is an organization committed to diversity and inclusion to drive our business results and create a better future every day for our diverse employees, clients, partners, and communities. We believe a diverse workforce allows us to match our growth ambitions and drive inclusion across the business. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Equal Opportunity/Affirmative Action Employer Minorities/Females/Protected Veterans/Persons 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Citian, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Citian AI Hiring
Citian has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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.