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About This Role
C2 Labs (www.c2labs.com) helps government and commercial organizations transform how they work through AI, automation, cloud, cybersecurity, and modern software. We build real products and platforms that remove manual work, speed decisions, and scale securely in regulated environments. If you want to ship AI that actually lands in production, not sit in a slide deck, this is the role.
Preferred to live in the Knoxville, Tennessee metro area (or willing to relocate).
Position Summary
C2 Labs is hiring a Senior Forward Deployed Engineer to design, build, and ship AI and technology products that change how teams operate. You will work close to the mission, turn messy real\-world workflows into working software and agents, and help define the next generation of our AI platform. This is a high\-ownership role for someone who loves AI, automation, and transformation — and wants to see their work used every day.
What You'll Do
- Build and ship AI\-powered products, agents, copilots, and automation that create measurable operational impact.
- Turn complex business and mission workflows into simple, reliable technology products.
- Design and implement modern AI architectures (agents, orchestration, RAG/knowledge systems, evaluation, and human\-in\-the\-loop controls).
- Integrate AI with enterprise systems, APIs, cloud services, and data sources so solutions work in the real world.
- Drive digital transformation by replacing slow manual processes with automated, intelligent workflows.
- Build with strong governance in mind: secure use of AI, clear human decision points, auditability, and responsible automation.
- Elevate customer experience and service: make tools intuitive, reliable, and easy for operators to adopt and trust.
- Support sales and business development with technical discovery, demos, solution framing, and credible delivery plans.
- Contribute across the stack as needed: software, data, cloud/infrastructure, DevOps, security, product and project management, and analysis.
- Raise the bar for quality: secure by design, observable, maintainable, and proven in production.
- Capture what works as reusable product patterns so the whole company moves faster.
What We're Looking For
- U.S. Citizenship required.
- Builder mindset: you ship products, not just prototypes.
- 5–8 years building software, platforms, cloud systems, or applied technology products (or equivalent intensity).
- 1–3 years shipping AI, automation, or LLM\-powered products in production.
- Hands\-on experience with AI/LLM systems (agents, RAG, automation, evaluation).
- Strong software engineering skills (Python required; modern web/TypeScript a plus).
- Cloud delivery experience (AWS and/or Azure), APIs, CI/CD, and Infrastructure as Code.
- Comfort spanning multiple domains: engineering, data, security, governance, product, and project delivery.
- Customer\-oriented mindset: experience and service quality matter as much as technical cleverness.
- Clear communication with technical and non\-technical stakeholders, including sales and business development conversations.
- Excitement about AI\-driven transformation in government contracting and government agency environments.
Nice to Have
- Bachelor’s degree in a technical field or equivalent practical experience.
- Working understanding of UX/UI design principles.
- Active Top Secret (TS) clearance preferred.
- Experience delivering technology in government contracting or government agency settings.
- Familiarity with compliance\-aware environments (FedRAMP, RMF, NIST, CMMC\-adjacent).
- Experience with Microsoft AI/automation ecosystems (Copilot Studio, Power Platform) or similar enterprise AI platforms.
- Prior forward deployed, field, or customer\-embedded engineering experience.
- Prior support of sales cycles, proposals, or business development technical pursuits.
Why C2 Labs?
You will help build an AI\-native product and engineering culture where automation is the default, transformation is the mission, and ownership is real. Small team. High impact. Work that matters in government contracting and across government agencies — with room to shape the products from day one.
EEO STATEMENT:
We are an equal opportunity employer. All qualified applicants will be considered without discrimination based on race, color, religion, sex, national origin, age, disability, or protected veteran status. Employment offers will be contingent on passing a pre\-employment drug screen.
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 c2 labs, 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.
c2 labs AI Hiring
c2 labs has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Knoxville, TN, 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
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