Head of AI Training Research
invisibletech • Worldwide - Remote
Posted: June 10, 2026
Job Description
About the Role
We are looking for a commercially-minded research leader to serve as Head of AI Training Research, responsible for delivering measurable client outcomes through applied research excellence. This role sits at the intersection of research and revenue — leading a team of applied researchers, forward deployment engineers (FDEs), and project leads who work in close coordination to ensure every client engagement is backed by rigorous methodology and drives tangible results.
This is not a purely academic role. You will be accountable for the quality, scalability, and commercial impact of our training research practice.
What You'll Do
Leadership & Commercial Accountability
- Lead and align a cross-functional pod of applied researchers, FDEs, and project leads around shared client outcome goals
- Own the research practice's contribution to revenue — including supporting pre-sales, scoping engagements, and ensuring delivery quality that drives retention and expansion
- Act as an executive sponsor on strategic accounts, providing research credibility and depth to client relationships
- Build a team culture that is rigorous, fast-moving, and relentlessly focused on client impact
Client Delivery & Applied Research
- Define the applied research frameworks, workflows, and quality standards used across all client engagements
- Ensure applied researchers are translating benchmarking, data, and RL environment work directly into client-specific training solutions
- Partner with project leads to scope engagements accurately, manage research risk, and hit delivery milestones
- Work with FDEs to ensure research outputs are deployable, integrated, and producing measurable model improvements in client environments
Benchmarking
- Oversee the development of benchmarking capabilities used to demonstrate value to clients — pre- and post-engagement performance comparisons, capability gap analyses, and regression tracking
- Ensure benchmark design is tied to client-defined success metrics, not just internal research goals
- Use benchmark outputs as a feedback loop to continuously improve delivery quality across engagements
OTS Data & Data Strategy
- Lead strategy around sourcing, filtering, and deploying off-the-shelf datasets in support of client training objectives
- Build repeatable frameworks for data quality assessment that can be applied efficiently across diverse client use cases
- Identify reusable data assets and pipelines across engagements to improve margins and delivery speed
RL Environment Building
- Oversee the development of RL environments that are purpose-built or adapted for client-specific task performance
- Ensure environments are reproducible and portable across client deployments
- Partner with FDEs and applied researchers to close the loop between environment design and real-world client outcomes
What We're Looking For
- 8+ years in applied ML or AI research, with at least 3 years leading research or technical delivery teams in a client-facing or revenue-generating context
- Proven track record of translating research capabilities — benchmarking, data curation, or RL — into delivered client value
- Experience working across applied researcher, engineering, and project management functions; comfortable orchestrating cross-functional pods toward a shared outcome
- Strong commercial instincts — able to scope, price, and communicate research work in terms of client ROI
- Deep familiarity with LLM training pipelines, including fine-tuning, RLHF/RLAIF, and evaluation methodology
- Excellent executive communication skills; confident representing the research practice in client conversations and during pre-sales
Nice to Have
- Prior experience at an AI services firm, applied research consultancy, or enterprise AI product company
- Familiarity with data licensing, synthetic data generation, or contamination detection in the context of client data
- Experience building scalable delivery infrastructure (templates, tooling, benchmarks) that improves team output across engagements
What’s In It For You
Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.
Additional Content
About the Role
We are looking for a commercially-minded research leader to serve as Head of AI Training Research, responsible for delivering measurable client outcomes through applied research excellence. This role sits at the intersection of research and revenue — leading a team of applied researchers, forward deployment engineers (FDEs), and project leads who work in close coordination to ensure every client engagement is backed by rigorous methodology and drives tangible results.
This is not a purely academic role. You will be accountable for the quality, scalability, and commercial impact of our training research practice.
What You'll Do
Leadership & Commercial Accountability
- Lead and align a cross-functional pod of applied researchers, FDEs, and project leads around shared client outcome goals
- Own the research practice's contribution to revenue — including supporting pre-sales, scoping engagements, and ensuring delivery quality that drives retention and expansion
- Act as an executive sponsor on strategic accounts, providing research credibility and depth to client relationships
- Build a team culture that is rigorous, fast-moving, and relentlessly focused on client impact
Client Delivery & Applied Research
- Define the applied research frameworks, workflows, and quality standards used across all client engagements
- Ensure applied researchers are translating benchmarking, data, and RL environment work directly into client-specific training solutions
- Partner with project leads to scope engagements accurately, manage research risk, and hit delivery milestones
- Work with FDEs to ensure research outputs are deployable, integrated, and producing measurable model improvements in client environments
Benchmarking
- Oversee the development of benchmarking capabilities used to demonstrate value to clients — pre- and post-engagement performance comparisons, capability gap analyses, and regression tracking
- Ensure benchmark design is tied to client-defined success metrics, not just internal research goals
- Use benchmark outputs as a feedback loop to continuously improve delivery quality across engagements
OTS Data & Data Strategy
- Lead strategy around sourcing, filtering, and deploying off-the-shelf datasets in support of client training objectives
- Build repeatable frameworks for data quality assessment that can be applied efficiently across diverse client use cases
- Identify reusable data assets and pipelines across engagements to improve margins and delivery speed
RL Environment Building
- Oversee the development of RL environments that are purpose-built or adapted for client-specific task performance
- Ensure environments are reproducible and portable across client deployments
- Partner with FDEs and applied researchers to close the loop between environment design and real-world client outcomes
What We're Looking For
- 8+ years in applied ML or AI research, with at least 3 years leading research or technical delivery teams in a client-facing or revenue-generating context
- Proven track record of translating research capabilities — benchmarking, data curation, or RL — into delivered client value
- Experience working across applied researcher, engineering, and project management functions; comfortable orchestrating cross-functional pods toward a shared outcome
- Strong commercial instincts — able to scope, price, and communicate research work in terms of client ROI
- Deep familiarity with LLM training pipelines, including fine-tuning, RLHF/RLAIF, and evaluation methodology
- Excellent executive communication skills; confident representing the research practice in client conversations and during pre-sales
Nice to Have
- Prior experience at an AI services firm, applied research consultancy, or enterprise AI product company
- Familiarity with data licensing, synthetic data generation, or contamination detection in the context of client data
- Experience building scalable delivery infrastructure (templates, tooling, benchmarks) that improves team output across engagements
What’s In It For You
Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.