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
The hiring risks we check — and the evidence we look for.
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
For a Research scientists in ai & machine learning, a CV scores on the flows it shipped and the research behind them — not on a keyword list.
Hiring for Research scientists? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksApplied research scientists
Mid · Research · AI & Machine Learning
A Applied 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
For a Research scientists in ai & machine learning, a CV scores on the flows it shipped and the research behind them — not on a keyword list.
Hiring for Applied research scientists? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksResearch engineers
Mid · Research · AI & Machine Learning
A Research engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for Research engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksHeads of research
Mid · Research · AI & Machine Learning
A Heads of research 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
For a Research scientists in ai & machine learning, a CV scores on the flows it shipped and the research behind them — not on a keyword list.
Hiring for Heads of research? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksML engineers
Mid · Applied ML engineering · AI & Machine Learning
A ML engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for ML engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksSenior ML engineers
Senior · Applied ML engineering · AI & Machine Learning
A Senior ML engineers at TaaSFlow is a senior operator who sets technical direction and mentors peers — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 5–8 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for Senior ML engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksStaff ML engineers
Lead · Applied ML engineering · AI & Machine Learning
A Staff ML engineers at TaaSFlow is a lead operator who leads a squad's delivery and cross-team coordination — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 7–10 years including squad leadership of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for Staff ML engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksML tech leads
Lead · Applied ML engineering · AI & Machine Learning
A ML tech leads at TaaSFlow is a lead operator who leads a squad's delivery and cross-team coordination — focused on turning warehouse data into decisions inside a ai & machine learning context.
Common requirements
- 7–10 years including squad leadership of relevant experience
- Pipelines or models in production, with owners and SLAs
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Pipeline ownership
- Model lifecycle
- Stakeholder impact
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- SQL / warehousing
- Modelling & experimentation
Likely validation areas
- Model / pipeline claims cross-checked against tooling exposure
- Employment continuity and reason for change
Sample evidence line
For a ML tech leads in ai & machine learning, a CV scores on the models or pipelines it names, and who kept them running — not on a keyword list.
Hiring for ML tech leads? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksMLOps engineers
Mid · MLOps & platform · AI & Machine Learning
A MLOps engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for MLOps engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksML platform engineers
Mid · MLOps & platform · AI & Machine Learning
A ML platform engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for ML platform engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksData & ML platform leads
Lead · MLOps & platform · AI & Machine Learning
A Data & ML platform leads at TaaSFlow is a lead operator who leads a squad's delivery and cross-team coordination — focused on turning warehouse data into decisions inside a ai & machine learning context.
Common requirements
- 7–10 years including squad leadership of relevant experience
- Pipelines or models in production, with owners and SLAs
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Pipeline ownership
- Model lifecycle
- Stakeholder impact
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- SQL / warehousing
- Modelling & experimentation
Likely validation areas
- Model / pipeline claims cross-checked against tooling exposure
- Employment continuity and reason for change
Sample evidence line
For a ML tech leads in ai & machine learning, a CV scores on the models or pipelines it names, and who kept them running — not on a keyword list.
Hiring for Data & ML platform leads? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksLLM engineers
Mid · GenAI & LLM · AI & Machine Learning
A LLM engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for LLM engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksApplied AI engineers
Mid · GenAI & LLM · AI & Machine Learning
A Applied AI engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for Applied AI engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksAI product engineers
Mid · GenAI & LLM · AI & Machine Learning
A AI product engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for AI product engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksPrompt engineers
Mid · GenAI & LLM · AI & Machine Learning
A Prompt engineers at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on shipping production software inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Production code shipped in the target stack, not just tutorials
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Years of production stack use
- System-design ownership
- Code review depth
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- System design
- Testing & CI/CD
Likely validation areas
- Stack claims cross-checked against project timelines
- Employment continuity and reason for change
Sample evidence line
For a Research engineers in ai & machine learning, a CV scores on the systems and stack it names, with dates and ownership scope — not on a keyword list.
Hiring for Prompt engineers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksAI product managers
Manager · AI product & leadership · AI & Machine Learning
A AI product managers at TaaSFlow is a manager operator who owns a team's outcomes, hiring and roadmap — focused on translating discovery into shipped roadmap inside a ai & machine learning context.
Common requirements
- 8–12 years with 2+ years managing a team of relevant experience
- Shipped roadmap with measurable outcome, not backlog grooming
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Outcomes attributed
- Discovery evidence
- Cross-functional traction
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- Discovery frameworks
- Prioritisation
Likely validation areas
- Outcome claims cross-checked against release cadence
- Employment continuity and reason for change
Sample evidence line
For a AI product managers in ai & machine learning, a CV scores on the launches it claims, with the decision it owned in each — not on a keyword list.
Hiring for AI product managers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksHeads of AI
Mid · AI product & leadership · AI & Machine Learning
A Heads of AI at TaaSFlow is a mid operator who owns delivery of individual tracks end to end — focused on turning warehouse data into decisions inside a ai & machine learning context.
Common requirements
- 2–5 years of relevant experience
- Pipelines or models in production, with owners and SLAs
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Pipeline ownership
- Model lifecycle
- Stakeholder impact
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- SQL / warehousing
- Modelling & experimentation
Likely validation areas
- Model / pipeline claims cross-checked against tooling exposure
- Employment continuity and reason for change
Sample evidence line
For a ML tech leads in ai & machine learning, a CV scores on the models or pipelines it names, and who kept them running — not on a keyword list.
Hiring for Heads of AI? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksVPs of ML
Executive · AI product & leadership · AI & Machine Learning
A VPs of ML at TaaSFlow is a executive operator who owns the function at the leadership table with P&L accountability — focused on turning warehouse data into decisions inside a ai & machine learning context.
Common requirements
- 15+ years with function-level accountability of relevant experience
- Pipelines or models in production, with owners and SLAs
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Pipeline ownership
- Model lifecycle
- Stakeholder impact
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- SQL / warehousing
- Modelling & experimentation
Likely validation areas
- Model / pipeline claims cross-checked against tooling exposure
- Employment continuity and reason for change
Sample evidence line
For a ML tech leads in ai & machine learning, a CV scores on the models or pipelines it names, and who kept them running — not on a keyword list.
Hiring for VPs of ML? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksChief AI officers
Executive · AI product & leadership · AI & Machine Learning
A Chief AI officers at TaaSFlow is a executive operator who owns the function at the leadership table with P&L accountability — focused on turning warehouse data into decisions inside a ai & machine learning context.
Common requirements
- 15+ years with function-level accountability of relevant experience
- Pipelines or models in production, with owners and SLAs
- Domain fluency for AI & Machine Learning
- Right to work confirmed for the target market
Candidate signals we score
- Pipeline ownership
- Model lifecycle
- Stakeholder impact
- Model portfolio
- Evaluation discipline
Relevant skills
- Deep learning
- Transformers
- RAG
- Fine-tuning
- SQL / warehousing
- Modelling & experimentation
Likely validation areas
- Model / pipeline claims cross-checked against tooling exposure
- Employment continuity and reason for change
Sample evidence line
For a ML tech leads in ai & machine learning, a CV scores on the models or pipelines it names, and who kept them running — not on a keyword list.
Hiring for Chief AI officers? Usually 5 business days from an approved brief.
Start a $699 pilotSee how it worksCraft
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
A ai & machine learning CV that names its systems & stack depth outright: the work, the dates, the scope it owned, and something a reference can confirm.
Watch-out
Systems & stack depth asserted for ai & machine learning with nothing named behind it — no dates, no scope, no way to tell individual work from team credit.
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.
Platform configuration
How TaaSFlow is configured for ai & machine learning hiring
Same platform, same objects, different configuration — Engineering and product. Depth of production skill carries the rubric; credentials carry very little.
Role families
What the workspace is set up to hire
Software engineering
Backend · Frontend · Full-stack · Mobile · Staff / principal
Platform and reliability
SRE · Platform · DevOps · Cloud architecture
Data and AI
Data engineering · Analytics engineering · ML engineering · Data science
Product and design
Product management · Technical PM · Product design · Research
Security
Application security · Cloud security · Detection & response · GRC
Requirements
Requirement patterns captured at intake
- Named languages, frameworks and clouds with years of production use
- Ownership scope: services owned, on-call, incident command
- Scale markers: traffic, data volume, users, cost envelope
- Work model and timezone overlap as a first-class requirement
Evidence
Evidence types extracted from the CV
- Systems owned
- Named services with production ownership, quoted from the CV.
- Architecture decisions
- Trade-offs stated on the CV, with the alternative rejected.
- Scale and reliability
- Latency, availability and incident numbers, not adjectives.
- Delivery record
- Shipped work with dates, scope and measurable outcome.
Scoring
Rubric weighting for this configuration
- Skills & tools
- 35
- Relevant experience
- 20
- Industry context
- 10
- Seniority & scope
- 15
- Credentials & licences
- 5
- Languages
- 5
- Location & logistics
- 10
- Skills sit at the ceiling of the allowed range because stack depth is the discriminator.
- Credentials sit at the floor: certifications rarely predict engineering outcomes.
- Adjacent stacks are scored as adjacency, with the gap stated rather than hidden.
Compliance
Compliance handled in the workflow
- Security programme exposure
- SOC 2, ISO 27001 or PCI-DSS scope recorded where the role touches it.
- Data handling
- GDPR-aware handling flagged for roles working on EU or UK personal data.
- Right to work and location
- Work authorisation and timezone captured as hard requirements when the role demands them.
Approval controls
Who has to agree before anything moves
Fast configuration: one reviewer, evidence verification on, no second approver on stage moves.
- Approval before client visibility
- No candidate appears in a client workspace until a reviewer approves them for that specific role.
- Evidence verification
- Extracted evidence is reviewable line by line, and a reviewer can confirm or reject each finding before it counts.
- Separate contact release
- Seeing a candidate and seeing their contact details are two different permissions, released independently.
- Reversible decisions
- Client decisions stay reversible for a short window, so a mis-click never becomes a permanent outcome.
- Full audit trail
- Every state change records who did it, when, and against which rubric version.
Integrations
Connections used in this configuration
- Agent connectivity (MCP)Available
- Transactional emailAvailable
- Microsoft TeamsBeta
- Intake and application endpointsCustom setup
Role blueprint — example
Senior backend engineer (example)
Example configuration output, not a customer role. Seniority: Senior.
Must-haves
- 4+ years production Go, Java or Node
- Owned a service in production with on-call responsibility
- Relational data modelling at scale
Dealbreakers
- No production ownership
- No overlap with the team's core hours
Screening questions
- Which production service did you own end to end, and what was its scale?
- Describe an architecture decision you made and the option you rejected.
Intelligence
What the recommendations layer watches here
- Requirement lists that are too restrictive for the available pool
- Score compression when every candidate looks the same
- Stalled technical interview stages
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.
Explore
Data & Analytics
Data engineering foundations that ML depends on.
Explore
FinTech
Applied ML for risk, fraud and personalisation.
Explore
SaaS
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.