Direct placement for the people building AI, and the people keeping it running.

Because most of our clients are manufacturers, distributors and industrial companies adding AI to a real operation, not software companies adding a tenth machine learning engineer. We know the plant, the data that comes off it and the technicians who keep the equipment running, and we recruit the engineer and the technician from one desk. If a role is purely software in a purely software company, we will tell you if a specialist is a better fit.

On the work, not the buzzwords. Which models they have taken to production, on what infrastructure, with what data, and what broke. We ask them to explain a past project to us the way they would explain it to your team, and we check references with people who were on that project. Your technical interviewers make the final call; our job is to make sure everyone you meet is worth the hour.

A fee only when you hire someone we presented. See how pricing works for the structure, and ask for the number in your written quote. For data center and critical facilities technicians we can also quote contract or temporary staffing on an hourly bill rate.

Yes, with sources. We publish national pay and demand data for AI and data roles in our monthly AI jobs report, drawn from BLS, Indeed Hiring Lab, CBRE and others, and we bring the relevant figures to the intake call. They are market data for setting a range, not a quote for any individual candidate.

Yes. C3 Workforce is a nationwide staffing and recruiting company headquartered in Livonia, Michigan, active in multiple markets across the country and doing business in all 50 states. Engineering and data searches run wherever the team sits, including remote and hybrid roles. Technician and facilities roles are staffed at the site.

Yes. Contract works well for a defined build: a data pipeline, a first model, a computer vision cell, an MLOps platform. The engineer works on C3 payroll with benefits at a fixed hourly rate for the term and can convert to your payroll whenever you decide. Direct placement is the better answer when the seat is permanent and the person will own the system for years. We quote both so you can compare the numbers before you choose.

Almost always with a data engineer, not a data scientist. A model is only as good as the data reaching it, and most plants have data scattered across the ERP, the MES, maintenance logs and spreadsheets. A data engineer builds the pipeline and the warehouse; then a data scientist or an applied machine learning engineer has something to work with. Data engineers are also the deepest candidate pool of any AI adjacent title, so the first search is the easier one. We will talk through your systems at intake and tell you which seat comes first.

Not for applied work. A PhD matters for research roles at frontier labs and for a small set of algorithm heavy problems. For forecasting, predictive maintenance, quality vision systems, pipelines and production machine learning, a strong engineer with a bachelor's or master's degree and a record of shipped systems will outperform a researcher who has never had to keep a model running on a Tuesday night. We screen for production experience first and degrees second, and we will tell you if a role genuinely needs the research profile.

They shift the pool toward people already in the United States. A $100,000 fee now applies to H 1B petitions for workers abroad, and the lottery is weighted toward higher wage levels; Penn Wharton estimates only 30 to 56 percent of fee subject registrations survive. Transfers of an existing H 1B are exempt from the fee, and candidates on OPT or STEM OPT remain sponsorable. In practice, a search that can consider H 1B transfers and recent US graduates has a wider field than one limited to citizens, and we help you understand which candidates fall where. Your immigration counsel makes the final call on sponsorship; we make sure you know the status of everyone on the short list.

Longer than a production order and shorter than doing it yourself, and it depends on the role, the range and how quickly your team interviews. No federal or institutional source publishes a time to fill for AI roles, and we will not invent one. What we do is give you a realistic timeline in the quote based on the specialty, the location and the range, tell you early if something about the search will slow it down, and keep both sides moving so a strong candidate does not accept another offer while your calendar fills.

Yes. AI product managers and AI governance leads are growing roles and neither requires a research network to recruit well. We screen product managers on a product they shipped and how they handled a model that underperformed, and governance candidates on policy and risk management experience plus credentials such as the IAPP AI Governance Professional certification. Both need credibility with engineers and plain English with executives, and we check for both.

Yes, and it is the part of AI hiring most tech recruiters cannot do. Data center technicians, critical facilities technicians, electricians, HVAC technicians and the controls and robotics technicians who keep AI driven equipment running on second shift are all roles we staff on direct placement, contract or temporary terms. Our roots are in industrial staffing and skilled trades, so the engineer and the technician come from the same desk with the same standard.