Interested in this AI/ML Engineer role at SimpliGov?
Apply Now →Skills & Technologies
About This Role
Role Overview:
We are building an engineering organization where autonomous agents move code from intent to production, while humans own judgment, standards, and direction. This role owns the delivery system that makes that safe.
You will evolve our existing release infrastructure into an AI\-enabled CI/CD system—automating coding pipelines, validation, orchestration, and reconciliation. The goal is to replace manual coordination with system\-enforced production readiness, dependency awareness, and release safety.
Responsibilities:
- Build and operate the agentic CI/CD stack: autonomous build loops, build gates, integration validation, progressive rollout, and rollback
- Extend our reconciliation layer so system truth is machine\-derived rather than human\-claimed: affected services, ticket\-to\-commit linkage, deploy\-vs\-release state, flag state
- Build the mission control layer: one live, machine\-derived picture of what is built, validated, deployed, released, and flagged, serving humans and agents alike
- Design the human\-in\-the\-loop model: when agents proceed autonomously, when they stop, who is escalated to, and with what evidence
- Instrument agent behavior end to end (traces, evaluations, failure and drift analysis) so every autonomous action is explainable and auditable
- Codify release policy as code: what ships, when, and with what proof
- Meter AI usage and cost across the pipeline; drive per\-workload routing and unit\-economics remediation
- Replace cross\-team coordination with contracts, dependency detection, and validation mechanisms
- Partner with DevOps and engineers on pipeline reliability, environment stability, and test coverage
- Evolve the operating process itself: how work flows from commit to validation to release without manual choreography, within a FedRAMP\-conscious compliance boundary
Qualifications:
- 5\+ years building engineering systems (CI/CD, internal platforms, developer tooling, or infrastructure) in SaaS or platform environments
- You write production code. This role builds and operates systems; it does not track them
- Hands\-on depth in pipeline design, integration failure analysis, dependency management, feature flags, and progressive delivery
- Experience operating LLM or agent systems in production, or clear evidence you will get there fast; using AI tools is table stakes, running them safely is the job
- Experience in compliance\-constrained environments (government, healthcare, finance) is a plus
- Strong systems thinking, operational rigor, and clear writing
Key Competencies
- Designs mechanisms, not meetings; when delivery breaks, your instinct is a gate, a test, or a signal, not a status field
- Treats the delivery system as a product whose users are engineers and agents
- Thinks in the open: surfaces uncertainty early, shows reasoning, owns outcomes
- Earns credibility with senior engineers through technical depth
- Balances autonomy and safety without introducing bureaucracy
What This Role Is NOT
This role is NOT a Scrum Master, NOT a PMO role, NOT a release coordinator, and NOT responsible for running Agile ceremonies or tracking projects. If your primary tools are Jira boards and planning meetings, this is not the role for you. If your instinct when delivery breaks is to add a meeting, a checklist, or a status field, this is not the role for you. If your instinct is to build a gate, a test, or a signal, keep reading.
How We Work
We run an AI\-native product development lifecycle. Autonomous agents participate in planning, coding, validation, and release; humans own judgment, standards, and direction. Work moves through a Plan\-and\-Review cadence rather than ceremony\-heavy Agile. Two standards are non\-negotiable: you own and can explain everything you ship, no matter what produced it, and you think in the open, surfacing uncertainty early rather than burying it.
What We Offer:
- Medical, dental, and vision insurance plans, with significant employer contributions for employees AND dependents (contributions based on base\-level plan; buyup plans available at additional costs)
- Company\-sponsored life \+ disability insurances
- 11 Paid holidays
- Flexible time off
- 401k plan with 4% employer match
- Monthly stipends for wellness and home office expenses
Legal Disclaimers:
*SimpliGov does* *not**sponsor applicants for work visas.*
*The US base salary range for this full\-time position begins at $130,000\.00 \+ bonus \+ benefits. Individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.*
*SimpliGov participates in the federal government's E\-Verify program, which confirms employment authorization of all newly hired employees and most existing employees through an electronic database maintained by the Social Security Administration and Department of Homeland Security. For new hires, the E\-Verify process is completed in conjunction with the Form I\-9 Employment Eligibility Verification on or before the first day of work; is not used as a prescreening tool.*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SimpliGov, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SimpliGov AI Hiring
SimpliGov has 3 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Based in Baltimore, MD, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.