Lead AI/ML Engineer

TacnIQ.ai is bridging AI and the physical world , building the foundation model for physical interaction, starting with tactile. We're creating a simple platform for engineers to build physical AI with any sensor, across wearables, robotics, and simulation. Our wearable data collection node is already live across five industries, logistics, construction, hospitality, e-commerce, and healthcare, feeding a pipeline that's turning raw sensor data into the foundation model nobody else has built.

This is a rare shot to work at the ground floor of a new category, on hardware and AI, on a problem that's genuinely unsolved, with a team that already has real-world traction to prove it.

Role Overview.

This role is central to turning that proof-of-concept into a structured, scalable research programme. There is no pre-defined roadmap. You will be expected to create it. You will lead data collection strategy, model architecture decisions, compute resource planning, and milestone setting. You will also mentor the Junior AI Engineer and serve as the senior technical voice for the AI track.

Key Responsibilities

· Define and own the Sensor FM research roadmap: data collection strategy, architecture selection, training pipeline, and evaluation framework.

· Lead model development from prototype to reproducible, documented pipelines.

· Propose and justify compute resource requirements (GPU/cloud) to leadership.

· Mentor the Junior AI Engineer and provide technical direction to interns.

· Serve as the primary technical interviewer for future AI hires.

· Engage with external partners, academic and research institutions, and potential customers on the R&D roadmap where needed.

Requirements

We value curiosity, ownership, and results over credentials. Our strongest AI contributor to date has been a final-year undergraduate. We care about what you can build and how you think, not just your CV.

· Strong fundamentals in machine learning, particularly in model training, fine-tuning, and evaluation.

· Experience with time-series or sensor data is a significant advantage.

· Demonstrated ability to take an ambiguous problem and produce a structured plan and working output.

· Comfortable with Python, PyTorch or JAX, and standard ML tooling.

· MLOps or deployment experience is a plus, particularly for constrained or embedded compute environments.

· Ability to communicate technical direction clearly to non-ML stakeholders.

Interview Process Note

Shortlisted candidates will be asked to submit a 1–2 page technical proposal.

The proposal should address the following prompt:

Given a suite of heterogeneous sensors and a small labelled dataset, how would you approach building a generalised Sensor Foundation Model that can be fine-tuned for application-specific tasks with less data?

The proposal should cover the key stages of the approach end-to-end, from data through to deployment, along with the key risks and how they would be tested.

Why Join TacnIQ.ai

Build the category, not just join it . We're building the foundation model for physical interaction, starting with tactile, across wearables, robotics, and simulation.

Real traction, not just a vision. Already live across five industries: logistics, construction, hospitality, e-commerce, and healthcare.

Real ownership. Work across hardware, firmware, AI, and go-to-market with a clear path to leadership for those who deliver.

Lean, flat, and human. Flexible by design, transparent by default, genuinely invested in your wellbeing and anyone can make a difference.

Equal Opportunity

TacnIQ.ai welcomes applicants from different educational, professional, and personal backgrounds.

If the role interests you but your experience does not match every requirement, we still encourage you to apply. What matters most is your ability to learn, build, communicate, and take ownership.