Tru.AI Talent · Powered by TruTalent Solution
Domain depth,
not generalist breadth.
Every search we run is in the AI and ML space. That focus compounds over time — into network depth, technical fluency, and a calibrated view of what good looks like at every level.
We don't cover every discipline. We cover four — and we cover them with a depth that generalist firms can't match.
7+
Years of AI/ML focus
Exclusive to this domain since 2024
IC → C-suite
Roles placed
Full spectrum, every seniority level
Retained only
Search model
No contingency, ever
Technical + cultural
Candidate assessment
Both dimensions, every shortlist
We know the ML talent landscape — not just the job titles.
Machine learning is not a monolith. The skills required to build a recommendation system are different from those needed to deploy a real-time inference pipeline or architect a multi-modal foundation model. We understand those distinctions — and we source accordingly.
Our depth
Our network spans the full ML spectrum: researchers who publish, engineers who ship, and the rare individuals who do both. We know where the best ML talent works, what they care about, and what it takes to get them to move.
Roles we place
ML Engineer (Senior / Staff / Principal)
Production ML systems, model deployment, inference optimization, feature engineering at scale.
Research Scientist
Novel model development, applied research, bridging academic work and product application.
LLM Engineer
Large language model fine-tuning, prompt engineering at scale, RLHF, evaluation frameworks.
Agentic AI Architect
Autonomous agent frameworks, multi-agent orchestration, tool-use systems, LLM-powered workflows.
MLOps / ML Platform Engineer
Training infrastructure, model registries, CI/CD for ML, feature stores, monitoring and observability.
How we assess
We assess model architecture decisions, not just framework familiarity
We distinguish research depth from production engineering strength
We understand the difference between fine-tuning and full pre-training
We know what "production ML" actually means at different company stages
Looking for a machine learning hire?
Start a searchData science hiring that goes beyond the resume.
The term "data scientist" covers an enormous range of actual work — from statistical modeling and experimentation to business intelligence, causal inference, and applied AI research. We don't treat it as a single profile. We understand the spectrum, and we find candidates who match the specific work your team actually does.
Our depth
We've placed data scientists at every stage — from seed-stage startups building their first analytics function to enterprise AI divisions running hundreds of experiments in parallel. That range gives us calibration that generalist recruiters simply don't have.
Roles we place
Data Scientist (Applied / Research)
Statistical modeling, experimentation, A/B testing, causal inference, predictive analytics.
Quantitative Analyst
Quantitative modeling, risk analysis, algorithmic strategy, financial data science.
Decision Scientist
Business-facing data science, optimization, decision modeling, cross-functional influence.
AI/ML Researcher
Applied research, novel algorithm development, publication-track work with product application.
Head of Data Science
Team building, research direction, stakeholder alignment, translating science into product.
How we assess
We evaluate statistical rigor, not just Python proficiency
We assess how candidates communicate uncertainty to non-technical stakeholders
We understand the difference between correlation analysis and causal inference
We know what "data science maturity" looks like at different org stages
Looking for a data science hire?
Start a searchThe infrastructure layer that makes AI products possible.
AI platform engineering is one of the most underrecruited disciplines in the market. These are the engineers who build the systems that every ML team depends on — the training infrastructure, the feature stores, the model serving layers, the data pipelines. Without them, AI roadmaps stall. Finding them requires knowing where to look.
Our depth
Platform and infrastructure engineers are rarely active on job boards. They're deep in systems work, often at companies that treat them well. Reaching them requires a network built over years and a technical conversation that earns their attention. That's what we bring.
Roles we place
Data Engineer
Data pipelines, ETL/ELT architecture, streaming systems, data lake and warehouse design.
ML Infrastructure Engineer
Training clusters, distributed compute, GPU orchestration, experiment tracking, model registries.
AI Platform Engineer
Internal ML platforms, developer tooling for data scientists, self-serve infrastructure.
Data Architect
Enterprise data strategy, schema design, data governance, cross-system integration.
Site Reliability / ML Systems Engineer
Model serving reliability, latency optimization, inference infrastructure, production observability.
How we assess
We understand distributed systems tradeoffs in the context of ML workloads
We know the difference between a data warehouse architect and a streaming pipeline engineer
We assess platform thinking — not just individual technical execution
We understand what "developer experience for ML teams" actually means in practice
Looking for a ai platform engineering hire?
Start a searchLeadership recruiting that doesn't confuse seniority with management.
Technical leadership is the hardest category to get right. The best technical leaders are strong enough technically to earn the respect of senior engineers, and strong enough as leaders to build, retain, and develop a team. Most recruiting processes optimize for one or the other. We assess both — and we don't confuse years of experience with actual leadership capability.
Our depth
We've placed technical leaders at every level — from first-time engineering managers stepping into leadership to Chief AI Officers defining the direction of billion-dollar AI programs. That range gives us a calibrated view of what good looks like at each stage.
Roles we place
Engineering Manager / Director
Team leadership, technical direction, hiring and retention, cross-functional execution.
Staff / Principal / Distinguished Engineer
Technical strategy, architecture decisions, org-wide influence, deep domain expertise.
VP of Engineering / VP of AI
Organizational design, roadmap ownership, executive alignment, multi-team leadership.
Head of AI / Head of ML
AI strategy, research direction, team building, product-science alignment.
CTO / Chief AI Officer / CDO
Executive technical vision, board-level communication, company-wide AI strategy and execution.
How we assess
We assess leadership philosophy, not just org chart position
We evaluate how candidates build psychological safety on technical teams
We understand the player-coach dynamic and when each mode is appropriate
We know what "technical credibility" means at Staff, VP, and C-suite levels respectively
Looking for a technical leadership hire?
Start a searchSpecialization isn't a positioning statement.
It's what makes the search better.
We know who to call.
Seven years of exclusive AI and ML focus means our network is deep, not wide. We know the engineers who are quietly excellent, the researchers who are ready to move, and the leaders who are open to the right conversation.
We know what to ask.
Technical fluency changes the quality of every conversation — with candidates and with clients. We can assess depth, not just credentials. We can ask the questions that reveal how someone actually thinks.
We know what good looks like.
Having placed across every level of the AI and ML stack, we have a calibrated view of what strong looks like at each role and stage. That calibration is what makes our shortlists different.
Looking for a specialist
in one of these areas?
Every search starts with a conversation. Tell us about the role and the problem you're trying to solve — we'll tell you if we're the right fit.