Impac Exploration Services

AI Safety & Transparency Engineer

AI Safety & Transparency Engineer
Notice info
LocationWeatherford, OK
Job Typefull time
Remote
Oil and Gas

About This Job


AI Safety & Transparency Engineer

Division: DATUM, Impac Exploration Services Location: Remote, Oklahoma City (OK), Houston (TX) Type: Full-Time

Build AI That Earns Trust by Deserving It

We're not building black boxes. At DATUM, our AI makes decisions about critical infrastructure where "trust me, it works" isn't good enough. When our models predict something, operators need to understand why. When they make recommendations, engineers need to see the reasoning.

We need someone who believes AI transparency isn't a nice-to-have—it's the whole game. Someone who gets excited about making complex models explainable without dumbing them down. Who sees safety not as constraints, but as design principles that make AI actually useful in the real world.


What You'll Build

Explainability frameworks that show why models make specific predictions
Safety systems that catch edge cases before they reach production
Transparency tools that build operator confidence in AI decisions
Testing protocols for AI in high-stakes industrial environments
Interpretability methods for complex multimodal models
Trust metrics that actually measure what matters


Your North Star

If an operator can't understand it, we haven't finished building it
The best AI safety happens at design time, not as band-aids
Transparency means glass box, not black box with documentation
Industrial AI has different stakes than consumer AI—act like it
Trust is earned through understanding, not compliance checkboxes
The most ethical AI is AI that actually gets used safely


The Technical Challenge

Make deep learning interpretable without sacrificing performance
Build explainability for decisions that combine physics, sensors, and ML
Create safety frameworks for environments where "undo" doesn't exist
Design transparency for users who are experts in drilling, not data science
Develop testing protocols for conditions you can't replicate in a lab
Balance "why did it do that?" with "what should I do now?"


You're Our Person If

You've made complex AI systems interpretable for non-technical users
You believe safety and performance aren't trade-offs
You can translate between ML researchers and field operators
You see AI ethics as engineering challenges, not philosophy debates
You've built explainability that actually explains
You understand that industrial safety is different from consumer safety


Especially If

You've worked on high-stakes AI (healthcare, autonomous systems, finance)
You've built interpretability for multimodal or time-series models
You understand both ML theory and human factors
You've designed AI systems that passed rigorous safety audits
You can make uncertainty quantification intuitive
You've turned "responsible AI" from concept to code


Why This Role Matters


Your work enables:

Operators to trust AI recommendations with million-dollar consequences
Regulatory acceptance of AI in critical infrastructure
Our research team to push boundaries while staying grounded
A new paradigm for industrial AI that's open, not opaque

This isn't about checking compliance boxes. It's about building AI that deserves to be trusted with decisions that matter.


The Reality


Industrial AI safety is uncharted territory. You'll:

Define standards that don't exist yet
Build explainability for users who've never trusted algorithms
Create safety frameworks for physics-based problems
Navigate between "move fast" and "don't break things that matter"
Translate academic interpretability research to industrial reality


Growth Path

Start: Build transparency into our existing models Six months: Define how industrial AI safety should work. One year: Publish frameworks others adopt

When companies realize they need industrial AI ethics expertise, you'll have written the playbook they're trying to follow.


The Challenge

You'll make AI that's powerful enough to transform industries and transparent enough for a roughneck to trust it at 3 AM. You'll prove that explainable doesn't mean weak, and safe doesn't mean slow.


Ready to Open the Black Box?

Show us explainability work that actually helped real users. Tell us about safety systems you've built for complex environments. Share your vision for industrial AI that's both powerful and trustworthy.

We're looking for someone who sees "it's too complex to explain" as a challenge, not an excuse.

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