Interested in this AI/ML Engineer role at Fearless?
Apply Now →Skills & Technologies
About This Role
Category: Other reqId: P-100279 Posted Date: 09/29/2026 Work Environment: Remote Exempt widget: Contract (1099) Location: Baltimore, Maryland, United States None No Travel
Job Requirements
AI Engineering Trainer (1099 Contractor)
At Fearless, we are seeking a 1099 contractor to serve as an AI Engineering Trainer for a 10-week intensive workforce development program focused on AI-enabled software engineering and modernization. The trainer will lead a cohort of experienced technical professionals through a structured curriculum designed by Team Fearless, combining live instruction, hands-on labs, technical demonstrations, collaborative projects, and individualized coaching.
This role requires both hands-on AI engineering experience and the ability to translate complex technical concepts into practical skills. The trainer will help participants design, develop, integrate, test, troubleshoot, and securely deploy AI-enabled and agentic AI solutions within realistic enterprise environments. Training will include AI engineering fundamentals, agentic AI, AI-assisted software development, Python, data and integration patterns, secure software delivery, responsible AI, and modernization of legacy systems. The curriculum is provided by Fearless; the trainer is not expected to develop the curriculum independently but may supplement it with additional examples, exercises, or resources that strengthen the learning experience. This is a fully remote 1099 contractor engagement requiring 40 hours per week for 10 weeks, with work performed between 9:00 a.m. and 5:00 p.m. EST
Your Responsibilities in this Role:
- Deliver live, instructor-led technical training focused on AI engineering, agentic AI, and AI-enabled software development.
- Lead hands-on labs, technical demonstrations, practical exercises, and collaborative projects that require participants to design, build, integrate, test, debug, and troubleshoot AI-enabled solutions.
- Teach and reinforce practical application of Python, AI/LLM tools and frameworks, APIs, data engineering concepts, source control, testing, and modern software delivery practices.
- Guide participants through AI-assisted engineering workflows, including code generation, testing, automation, debugging, and legacy application modernization.
- Provide instruction and coaching on prompt engineering, Retrieval-Augmented Generation (RAG), AI orchestration patterns, and agentic AI concepts as applicable to the approved curriculum.
- Reinforce secure AI integration, responsible AI practices, data governance, privacy, security, and risk mitigation throughout technical exercises and projects.
- Provide real-time technical assistance during labs and project work, including troubleshooting, debugging, code review, and problem-solving support.
- Review participant technical work and provide individualized feedback, coaching, and remediation to strengthen identified skill gaps.
- Assess participant progress through labs, assignments, knowledge checks, technical demonstrations, and capstone activities.
- Support team-based capstone work requiring participants to demonstrate an AI-enabled or agentic solution within a realistic enterprise modernization scenario.
- Collaborate with program leadership and other instructors to monitor participant progress, identify areas requiring additional reinforcement, and maintain alignment with the approved training plan.
Work Experience
Project Specific Skill and Experience Requirements
- Minimum of 5 years of professional experience in AI/ML engineering, software engineering, data engineering, or a closely related technical discipline.
- Demonstrated hands-on experience designing, developing, deploying, troubleshooting, optimizing, or maintaining AI and/or agentic AI solutions in production or comparable enterprise environments.
- Demonstrated experience delivering technical instruction, hands-on training, developer enablement, mentoring, coaching, or workforce development for adult technical learners.
- Strong working knowledge of Python and modern AI/LLM development tools, frameworks, and engineering workflows.
- Experience with AI-enabled application development, including APIs, data integration, testing, debugging, and secure software development practices.
- Experience with generative AI and modern AI engineering patterns such as prompt engineering, RAG, AI orchestration, and/or agentic AI frameworks.
- Ability to review participant code and technical artifacts, diagnose implementation challenges, and provide actionable technical feedback.
- Experience teaching through hands-on labs, projects, demonstrations, and applied exercises rather than lecture-only instruction.
- Strong communication skills with the ability to explain complex AI and software engineering concepts to learners with varying levels of technical experience.
Preferred Experience
- Experience developing or deploying AI solutions within federal, financial, tax-processing, or similarly regulated enterprise environments.
- Experience applying AI-assisted engineering techniques to legacy application modernization.
- Experience with enterprise data platforms, cloud environments, CI/CD, DevSecOps, or secure AI deployment.
- Familiarity with Databricks, Pega, Salesforce, Palantir, or comparable enterprise platforms.
- Experience with AI governance, responsible AI, data governance, security, privacy, and compliance requirements.
- Experience supporting team-based technical capstones, bootcamps, apprenticeships, or accelerated workforce development programs.
- Familiarity with Learning Management Systems (LMS) and participant progress tracking.
Physical Requirements:
- Ability to sit for extended periods while working on a computer or during meetings.
- Must be able to travel occasionally to client sites or company meetings, which may involve driving or flying.
- Ability to communicate effectively via phone, email, and in-person, requiring clear speech, listening, and written communication skills.
- Ability to move within an office environment, including reaching for files, using office equipment, and occasional light lifting (up to 10 pounds).
Benefits
Life at Fearless
We're a digital integration consultancy on a mission to build a better tomorrow. At Fearless, we combine technology, people, and organizational development to solve meaningful problems. Through iterative development, we deliver smart, user-friendly solutions that make technology work better—for everyone.
But great technology is only part of the story. What truly sets Fearless apart is our Purple Culture and our commitment to building meaningful partnerships with the people who help us deliver exceptional results.
What Makes Us Purple?
Whether you're a team member, contractor, or partner, our Purple Culture influences how we collaborate, communicate, and solve problems together.
Being Purple means you:
- Contribute to work that creates meaningful impact
- Collaborate with people who value curiosity, innovation, and continuous improvement
- Bring your expertise to challenging and rewarding projects
- Work with transparency, trust, and mutual respect
- Help create solutions that improve the lives of the people who use them
- Join a community committed to equity, inclusion, and belonging
We're proud to be an equal opportunity organization. We believe diverse perspectives make us stronger, and we welcome individuals from all backgrounds, experiences, and identities.
Compensation & Engagement Details
Fearless is committed to offering competitive contract rates that reflect the expertise, experience, and value our independent contractors bring to client engagements. Final rates are determined based on experience, qualifications, project requirements, and market considerations.
This position is structured as an independent contractor (1099) engagement and is not eligible for employee compensation programs or benefits.
Why Partner with Fearless?
When you partner with Fearless, you'll have the opportunity to:
- Contribute to mission-driven work that delivers meaningful outcomes
- Collaborate alongside experienced professionals across technology, delivery, design, and organizational development
- Apply your expertise to impactful client engagements
- Build relationships with a values-driven organization committed to excellence
- Work with a team that values autonomy, accountability, and results
- While independent contractors are not eligible for employee benefits, we strive to create strong partnerships that support successful outcomes for both our clients and our contractor community.
Independent Contractor Requirements
To engage with Fearless as an independent contractor, the following documentation must be provided prior to the start of work:
- Completed Form W-9
- Proof of General Liability Insurance
- Certificate of Insurance (COI)
- Compliance with any project-specific customer requirements, including background checks or security clearances, when applicable
Additional documentation may be required depending on the nature of the engagement.
Reasonable Accommodations
Fearless is committed to providing reasonable accommodations for applicants and candidates with disabilities. If a reasonable accommodation is needed to participate in the application or interview process, please contact the Human Resources Department at [email protected].
So, What's Next?
We've refined our engagement process to ensure alignment between Fearless, our clients, and our contractor partners.
If there's mutual alignment, we'll reach out to begin the interview and evaluation process. Depending on the role or project, the process may vary slightly, but typically includes:
- Introductory Conversation
We'll connect to:
- Learn more about your experience and expertise
- Discuss project requirements and expectations
- Review rate expectations and engagement details
- Answer any questions about Fearless and the opportunity
- Skills Assessment (If Applicable)
For some engagements, we'll ask you to complete a role-specific assessment or work sample to better understand your capabilities and approach.
- Skills & Business Fit Conversation
This conversation allows us to:
- Discuss relevant experience and project examples
- Explore your approach to problem-solving and collaboration
- Ensure alignment between your expertise and the engagement's needs
- Some engagements may also require customer interviews, background checks, or security clearance verification based on project requirements.
Where You Can Partner with Fearless
At Fearless, we’re proud to partner with talented independent contractors across the United States. We are currently able to engage 1099 contractors residing in all 50 states. For contractors who may be interested in future full-time employment opportunities with Fearless, please note that our current W-2 employment footprint includes the following states:
Alabama, California, Delaware, District of Columbia, Florida, Georgia, Maryland, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, and Virginia.
While contractor engagements are available nationwide, eligibility for future W-2 opportunities is contingent upon residing in one of our approved employment states at the time of consideration.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fearless, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Fearless AI Hiring
Fearless has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Baltimore, MD, US, Baltimore, MD.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.