Model & ML
ML engineers, applied scientists, evaluation talent, and training or fine-tuning hires tied to product goals.
TTR supports AI-native and AI-enabled companies hiring across model development, inference infrastructure, product surface area, and technical GTM where mandate clarity matters as much as speed.
Strong candidates filter quickly for role clarity, deployment seriousness, and whether the company knows what should be hired first.
ML engineers, applied scientists, evaluation talent, and training or fine-tuning hires tied to product goals.
MLOps, inference, platform, data, and reliability roles keeping AI products stable under real usage pressure.
AI product leaders, full-stack builders, and technical PMs translating model capability into repeatable software value.
Technical GTM, solutions, and customer-facing operators who can sell and deploy AI credibly.
The best candidates are usually already employed, over-contacted, and filtering hard on whether the company knows what it actually needs. Weak role design shows up quickly in process drift, compensation mismatch, and candidate drop-off.
Teams blur research, infra, and product responsibilities into one unrealistic AI req.
Clear role sequencing, technical calibration, and a hiring story grounded in production reality.
A startup guide to building an AI engineering team, including MLOps, data, product, infrastructure, and applied ML role sequencing.
Read the article →A startup guide to building an AI engineering team, including MLOps, data, product, infrastructure, and applied ML role sequencing.
Open signal →Why AI talent is hard to recruit in 2025 and 2026, and how startups can compete for ML, MLOps, and applied AI engineers.
Open signal →Why AI startups struggle to compete with Google and OpenAI for ML talent, and how compensation, equity, and ownership change the pitch.
Open signal →The engineering roles applied AI companies need to scale, from product-minded ML engineers to data, platform, and MLOps hires.
Open signal →Compare AI and ML recruiting agencies by passive network reach, technical evaluation depth, fee model, and startup fit.
Open signal →How long it takes to hire a senior MLOps engineer and what founders can do to reduce search friction and offer risk.
Open signal →What an MLOps engineer does, why the role is hard to fill, and how AI startups should evaluate production ML infrastructure talent.
Open signal →