AI & Machine Learning · Digital Systems
Applied AI hiring, calibrated between research, engineering and platform.
Applied AI, machine learning, MLOps and research hiring — with rubrics that separate research signal from production ML engineering and platform delivery.
Evidence quoted from the CV · rubric versioned per role level · 3 evaluation criteria
Hiring reality
AI & Machine Learning hiring challenges
What AI & Machine Learning teams tell us before switching to a structured, evidence-based workflow — and how TaaSFlow turns each risk into a scoring signal.
Research vs production drift
Publications and Kaggle badges do not equal production ML. Rubrics score each track on the outcomes it actually owned — inference latency, training pipelines, evaluation harnesses or published research.
Frontier vs applied
Frontier-model research and applied LLM engineering demand different evidence. We capture which layer of the stack the candidate lives at.
Evaluation rigour
Model quality is only credible with evaluation. We look for named benchmarks, offline test sets, A/B design and monitoring — not vibes.
Compute and cost fluency
Serving GPUs at production scale is a distinct skill. Rubrics capture training-cost, inference-cost and quantisation experience explicitly.
Role explorer
Explore AI & Machine Learning roles TaaSFlow sources
Select a family to see typical roles, common requirements, the signals we evaluate, and a sample of the evidence we quote back.
Research scientists
Mid · Research · AI & Machine Learning
A Research scientists at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shaping the experience customers judge you on inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Case studies with problem, decisions and after-metrics
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Portfolio depth
- Research rigour
- Systems thinking
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- Interaction design
- User research
Likely validation areas
- Portfolio ownership vs. team credit line
- Employment continuity and reason for change
Sample evidence line
“Rebuilt the checkout flow: task success +22%, case study links to before/after metrics and the research plan.”
Hiring a Research scientists? Brief the role — first shortlist within 7 business days.
Brief this roleSee how we source itCraft
Skills, tools and certifications
Skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- MLOps
- Distributed training
- Model serving
- Evaluation design
Tools & platforms
- PyTorch
- JAX
- HuggingFace
- LangChain
- LlamaIndex
- Ray
- Weights & Biases
- MLflow
- Kubeflow
- Vertex AI
- SageMaker
- Databricks
- vLLM
- Triton
How TaaSFlow scores talent
Scoring priorities for AI & Machine Learning
Every point of the score maps to an evidence quote from the CV. Dimensions, weights and critical requirements are shown alongside each candidate — the score supports judgment, it doesn't replace it.
What we evaluate in technology hires
Dimensions specific to AI & Machine Learning — not a generic checklist.
Dimension
Systems & stack depth
Years of production use of the actual stack the role touches — languages, frameworks, cloud, database — separated cleanly from tools merely listed on the CV.
Strong signal
6 years shipping Go/Postgres services on AWS with on-call ownership and named SLOs.
Watch-out
Long tool list with no matching project narrative or production timeline.
How TaaSFlow validates
Every stack claim is cross-checked against project timelines and named systems on the CV; surface exposure never scores as production experience.
Other AI & Machine Learning dimensions
See the full methodology on how scoring works.
Process
The AI & Machine Learning hiring process
See the full process on how it works.
Product demonstration
What a AI & Machine Learning shortlist looks like
Ranked candidates with a fit score, requirement coverage, evidence quotes, strengths and validation areas. Reviewed by a partner before it reaches you.
Example data — not a live candidate
AI & Machine Learning shortlist · Example
Candidate #EXAMPLE · Alex R.
Applying as: Research scientists
- Deep learning
- Transformers
- RAG
Recommended: shortlist
“Models trained, benchmarks owned, production traffic served — with named datasets and framework.”
Example data — no production candidate.
Adjacent hiring
Related industries
Technology
Engineering and platform teams shipping AI features.
ExploreData & Analytics
Data engineering foundations that ML depends on.
ExploreFinTech
Applied ML for risk, fraud and personalisation.
ExploreSaaS
Recurring-revenue hiring for product-led and enterprise SaaS teams — product, CS, RevOps, sales and implementation, cali
ExploreCommon questions
AI & Machine Learning hiring FAQ
Do you separate research and engineering candidates?
Yes — different rubrics, different evidence, never merged in ranking.
Can you hire specifically for LLM/GenAI roles?
Yes. Fine-tuning, RAG, evaluation and agent-framework experience are captured as first-class signals.
How do you validate production ML claims?
We quote CV lines describing production traffic, latency, model size and monitoring — not just tool names.
AI & Machine Learning
Hiring in AI or ML?
Submit the role — track-specific rubric, ranked shortlist, evidence you can defend.
- 20-minute discovery call — role, must-haves, timeline, budget.
- Ranked shortlist in 14 days — with evidence quoted from every CV.
- Flat subscription — no percentage-of-salary fees, ever.