ML & AI Engineering Specialty

    AI & ML Engineering Talent

    A capability overview of the AI and ML engineering roles we recruit: what each one owns, how the talent pools differ, and where teams usually mis-scope the hire.

    Hiring machine learning engineers specifically? See Machine Learning Recruiters. Building a full AI org or hiring AI leadership? See our AI recruiting practice.

    14 days
    Average kickoff to signed offer
    48 hours
    From kickoff to first shortlist
    90 days
    Replacement guarantee on placements
    6 pools
    Distinct AI/ML engineering talent pools

    Roles We Specialize In

    Individual-contributor and staff-level ML, AI, and platform engineers who ship in production.

    Machine Learning Engineers

    ML engineers who design, train, and deploy production models — from feature pipelines and training loops to online serving at scale.

    Applied AI Engineers

    Practitioners who ship LLM, RAG, and agent features into product — turning research prototypes into reliable user-facing systems.

    Data Scientists

    Statistical modelers and experimentation leads who drive product decisions, build predictive models, and own the analytics stack.

    MLOps Engineers

    Platform engineers who own model training infrastructure, CI/CD for ML, feature stores, evaluation harnesses, and production monitoring.

    AI Infrastructure Engineers

    Systems engineers who build GPU clusters, inference platforms, distributed training, and low-latency serving for foundation-model workloads.

    Founding AI Infrastructure Engineers

    First infrastructure hires for AI startups who design cost-efficient serving, training pipelines, and platform foundations from day one.

    Hiring AI or ML engineers?

    Tell us the roles, stack, and stage. We will map the search to the right talent pool and give you a realistic timeline in a 30-minute call.

    Book a Hiring Strategy Call