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How much do safe superintelligence jobs pay per hour?

As of Aug 25, 2026, the average hourly pay for safe superintelligence in Massachusetts is $18.69, according to ZipRecruiter salary data. Most workers in this role earn between $16.78 and $18.89 per hour, depending on experience, location, and employer.

What is a safe superintelligence?

A Safe Superintelligence refers to an artificial intelligence system that vastly surpasses human intelligence while being designed to operate safely and align with human values. The primary goal is to ensure that as AI systems become more powerful, they do not act in ways that could harm humanity or cause unintended consequences. Research in this field focuses on technical challenges like alignment, robustness, and interpretability, as well as policy and governance issues. Safe Superintelligence is a critical area of study for the long-term impact of AI on society.

What are some common challenges faced by professionals working on safe superintelligence projects, and how can they be addressed?

Professionals working on Safe Superintelligence projects often face challenges such as aligning AI goals with human values, managing uncertainty in advanced system behaviors, and collaborating across interdisciplinary teams. Addressing these challenges requires strong communication skills, a commitment to ongoing learning about both AI safety and technical advances, and proactive engagement with ethicists, policymakers, and domain experts. Many teams implement regular cross-functional meetings and robust review processes to ensure safety protocols are developed and followed. Staying updated with the latest research and participating in relevant workshops or conferences also helps professionals remain effective in this rapidly evolving field.

What are the key skills and qualifications needed to thrive as a safe superintelligence engineer, and why are they important?

To excel as a Safe Superintelligence Engineer, you need an advanced background in computer science, mathematics, and AI safety research, usually supported by a master's or Ph.D. in a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), formal verification tools, and safety-focused protocols is essential. Exceptional problem-solving, ethical reasoning, and clear communication skills help you navigate complex safety challenges and collaborate across multidisciplinary teams. These skills ensure that superintelligent systems are designed, implemented, and monitored in ways that minimize risks and align with human values.

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Infographic showing various Safe Superintelligence job openings in Massachusetts as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $38,867 per year, or $18.7 per hour.

Discovery Portfolio Manager

Physical Superintelligence

Boston, MA • On-site

$180 - $260/hr

Other

Posted 22 days ago


Job description

Overview

Physical Superintelligence is an exceptionally well-funded seed-stage startup with roots at Google, Meta, DeepMind, NVIDIA, Citadel, Harvard, MIT, Johns Hopkins, the Perimeter Institute, and the Institute for Advanced Study, building AI systems to discover new physics at scale.

Our mission is to discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence, safely, verifiably, and for broad public benefit.

Our platform runs discovery campaigns against open problems in physics. Those campaigns produce results, and someone has to decide what happens to them.

That turns out to be harder than it sounds. A result you put in a paper may be a result you have given away. A result you put in a patent is one you cannot talk about for a year and a half. Plenty of results that look like breakthroughs have no patentable subject matter in them at all, and telling those apart quickly is most of the skill. We need someone who owns the portfolio of problems we point the platform at, sets the bar a result has to clear before anyone outside the building sees it, and works out — case by case — whether it should become a publication, a filing, a trade secret, or open source. The final call on the genuinely hard ones sits with a standing committee. The recommendation, the reasoning, the record, and the follow-through are yours.

You are not the physicist on these results and you are not the lawyer on these filings. You are the person who turns what the platform produces into assets that last, instead of a pile of interesting outputs.

Role and Responsibilities

Point the platform at the right problems. You will map the landscape of open physics problems our system can actually reach and decide where to spend campaign time. That means running the cycle of cheap broad probes to find fertile ground, then concentrating effort where the ground turns out to be fertile, then killing what is not converting. The best problems scientifically are usually not the best problems commercially, and you will be making that trade constantly, in front of people who care about it. The portfolio is a live allocation decision, not a wish list, and you own defending it.

Decide what happens to what comes out. Some results are obviously scientific and some are obviously commercial, but most are both or neither, and that is where the value and the mistakes are. The way through is a written rule set that handles the recurring patterns so that only genuinely novel cases go to the committee, and so that fewer cases are novel each quarter. The routing question is not whether something feels scientific — our best results will be both. It is whether there is eligible subject matter at all or only a law of nature with the value sitting in a downstream claim; whether a competitor could reverse‑engineer it from what we ship, since a trade secret protects nothing if they can and a patent just teaches them if they cannot; whether a contract we signed before the result existed has already decided the question; and whether we could realistically detect and pursue infringement. You will read our commercial agreements as closely as you read our physics. One rule has no exceptions: filings come before disclosure, because most of the world grants no grace period and an early preprint gives away international rights for good.

Get results validated, then get them published. You will build a standing bench of outside experts across the subfields our campaigns touch — people whose review actually means something — and route provisional results through them before anything goes further. That network needs infrastructure to be safe: NDAs, export screening, conflict screening, and a protocol that keeps a reviewer from contributing an inventive idea and clouding title. Then you take what survives to strong venues and manage it through submission, referees, and revision. Publication is how the field learns to take us seriously, which is worth a great deal to us. You are accountable for what the machine produces, not for being on the byline.

Build the IP pipeline. Right now there isn't one. You will stand up disclosure intake, triage, and a filing cadence, and direct outside counsel on strategy and priority while they draft and prosecute. You will keep the portfolio pointed at where value is actually captured rather than where papers are easiest, hold the boundary against our open‑source releases, and keep it in a shape an investor's technical diligence team can follow without you in the room. Part of this is unglamorous and load‑bearing: current USPTO guidance requires that a natural person significantly contributed to the conception of an invention, and the more of the work our platform does, the more that record matters. It has to be built into how campaigns run, because it cannot be reconstructed later. If nobody does it, we end up with papers and no patents.

What We’re Looking For

Real technical depth in physics or an adjacent field. Enough to read a campaign output and judge whether it is real, novel, and significant, and enough to hold your own with our research team. A PhD is the most common way to get there and it is not the only one; a serious research career, or years of hands‑on technical work close to the physics, can do the same. What we cannot work with is someone who needs the result explained to them before they can classify it.

A technology transfer and IP management background. You have run the path from result to filing to publication yourself, with real stakes, and you know the places it goes wrong. Knowledge or technology transfer at a national lab, a research institution, or a university office; in‑house IP strategy at a deep tech company; managing a prosecution docket and the counsel attached to it. Advising on this from the outside is not the same thing.

Portfolio judgment. You have run a research program with a real prioritization mechanism and a real kill mechanism, under a budget and against a clock. You can tell us about something you shut down while it was still interesting.

A network, not a Rolodex. You have standing relationships with credible people across several physics subfields, you know who is worth listening to and who is merely loud, and those people will take your call.

You know when there is no patent to be had. You can look at a result and tell whether there is eligible subject matter in it or whether you are looking at a law of nature in an apparatus costume. You have had that argument with a scientist who was sure their discovery was patentable, and you were the one who was right.

The nerve to hold a line you cannot enforce by title. You will be under pressure from every direction to let something out early, and the pressure will be reasonable every time. Your leverage is the rule set and your credibility, not your seniority. You have held a line like that before without losing the relationship.

Clear eyes about AI‑generated science. You know where these systems produce insight and where they produce plausible nonsense, and you treat verification as the thing that separates the two. You do not need to build the platform. You do need to be unimpressed by it until it earns otherwise.

Nice to Have
  • Editorial or program committee experience at a major venue.

  • Experience with export control compliance and sensitive technology research and development.

  • Named inventor on issued patents.

  • Familiarity with the commercial domains we work in: compute infrastructure on the ground and in orbit, energy, and sensing and instrumentation.

How We Work

We are engineering‑led. Engineers own problems end to end, from spec to ship to on‑call. We write contracts before logic, test against real systems instead of mocks, and favor simple designs that ship over clever ones that do not. Our development process is AI‑native and we expect the discovery process to run on the same principles: instrumented, agent‑augmented, and measured.

We are a Public Benefit Corporation, and we run an academic grants program. The public benefit commitment is not decoration — it is a live input into the decisions this role handles, and you should expect to argue about it.

Location and Compensation

This role is based in Boston and is on‑site at our Cambridge hub. We offer competitive compensation including salary, benefits, and meaningful early‑stage equity. We evaluate judgment, what you have built, and the leverage you create.

This role involves access to controlled unclassified technical data, and some responsibilities are subject to U.S. export control regulations; we will discuss any resulting requirements transparently during the process. We are an equal opportunity employer and value diverse perspectives in building platforms for AI‑driven discovery.

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