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Algorithm Scientist Jobs (NOW HIRING)

Applied Materials

Algorithm Developer

Santa Clara, CA · On-site

$184K - $253K/yr

If you want to push the boundaries of materials science and engineering to create next generation ... You will perform algorithmic concept and feasibility for image processing algorithms, including ...

Toyon

Algorithm Developer

Goleta, CA · On-site

$120K - $200K/yr

Toyon is seeking Algorithm Developers capable of developing advanced algorithms and software for ... Requirements Candidates should have a strong background in engineering, computer science, physics ...

Toyon Research Corporation

Algorithm Developer

Goleta, CA · On-site

$120K - $200K/yr

Toyon is seeking Algorithm Developers capable of developing advanced algorithms and software for ... Requirements Candidates should have a strong background in engineering, computer science, physics ...

Applied Materials

Algorithm Developer

Santa Clara, CA · On-site +1

$184K - $253K/yr

If you want to push the boundaries of materials science and engineering to create next generation ... You will perform algorithmic concept and feasibility for image processing algorithms, including ...

Toyon

Algorithm Developer

Fort Collins, CO · On-site

$100K - $190K/yr

Toyon is seeking Algorithm Developers capable of developing advanced algorithms and software for ... Requirements Candidates should have a strong background in engineering, computer science, physics ...

Jobtailor

Quantum Algorithms Scientist

Washington, DC · On-site

PhD (or soon to receive one) in quantum information, quantum computation, computer science, physics, or a closely related field. * Extensive experience in implementing quantum algorithms on quantum ...

Amat

Algorithm Developer

Santa Clara, CA · On-site +1

$184K - $253K/yr

If you want to push the boundaries of materials science and engineering to create next generation ... You will perform algorithmic concept and feasibility for image processing algorithms, including ...

Aptiv

ADAS Algorithm Developer

Troy, MI

Summary We are currently looking for an ADAS Algorithm Developer to join our team in Troy, Michigan ... Science, or a related field. * 2+ years of experience in ADAS, Automated Driving, or automotive ...

GRVTY

Imagery Scientist (EO) - Senior

Falls Church, VA · On-site +1

$160K - $190K/yr

Algorithms * Scientific workflows * Exploitation methodologies * Experience automating scientific processing workflows * Experience developing and evaluating: * Algorithms * Automated workflows

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Algorithm Scientist information

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$50.5K

$111.3K

$137.5K

How much do algorithm scientist jobs pay per year?

As of Sep 13, 2026, the average yearly pay for algorithm scientist in the United States is $111,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $137,000.00 per year, depending on experience, location, and employer.

What is an algorithm scientist?

Algorithm Scientists are professionals who design, analyze, and optimize algorithms to solve complex computational problems. They often work in fields like data science, artificial intelligence, finance, and engineering, developing new methods or improving existing ones for processing data efficiently. Their work involves rigorous mathematics, computer science, and research to ensure algorithms are accurate, scalable, and effective for specific applications. Algorithm Scientists may also collaborate with software engineers to implement their solutions in real-world systems.

How does an algorithm scientist typically collaborate with cross-functional teams during the development process?

Algorithm Scientists frequently work alongside data engineers, software developers, and product managers to design and implement solutions. They are often responsible for communicating complex mathematical concepts in accessible terms to ensure alignment across the team. Regular meetings and code reviews are common, allowing for feedback and rapid iteration. This collaborative environment helps ensure that the algorithms developed are both technically sound and practically viable for real-world applications.

What are the key skills and qualifications needed to thrive as an algorithm scientist, and why are they important?

To thrive as an Algorithm Scientist, you need a strong background in mathematics, statistics, and computer science, often supported by an advanced degree such as a Master's or Ph.D. in a related field. Proficiency with programming languages like Python or C++, machine learning libraries (e.g., TensorFlow, PyTorch), and experience with data analysis tools are typically required. Strong problem-solving abilities, analytical thinking, and effective communication skills help distinguish top performers in this role. These skills are vital for developing innovative algorithms that solve complex problems, ensuring practical, scalable solutions in technological environments.
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Algorithm Scientist jobs near you
Infographic showing various Algorithm Scientist job openings in the United States as of September 2026, with employment types broken down into 74% Full Time, and 26% Contract. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $111,343 per year, or $53.5 per hour.

Research Scientist - Frontier AI/ML & Quantum Algorithms

San Francisco, CA • On-site

Sygaldry
See more jobs at Sygaldryexternal

$200K - $300K/yr

Full-time

Medical, PTO

Re-posted 7 hours ago

Job description

About Sygaldry
Sygaldry Technologies is building quantum-accelerated AI servers to exponentially speed up training and inference for AI. By integrating quantum and AI, we're accelerating the path to superintelligence, and addressing the problem of rising compute costs and energy bottlenecks. Sygaldry AI servers combine multiple qubit types within a single, fault-tolerant architecture to deliver the combination of cost, scale, and speed necessary for advanced AI applications. We pioneer new domains in physics, engineering, and AI, tackling the hardest challenges with a grounded, optimistic, and rigorous culture. We're looking for individuals ready to define the intersection of quantum and AI and drive its profound global impact.
About the Role
Frontier AI is moving toward scientific reasoning and design: molecules, materials, proteins, weather, climate, dynamical systems, quantum devices, and controlled physical systems. These domains expose deep computational bottlenecks in sampling, probabilistic inference, optimization, simulation, uncertainty quantification, inverse design, planning, and control.
Sygaldry is building quantum-accelerated AI systems for this next era. We are looking for a Research Scientist who can help define Quantum AI: not just quantum machine learning, but the broader study of how fault-tolerant quantum computation can transform the primitives of learning, inference, reasoning, prediction, geometry, and control.
In this role, you will work at the intersection of frontier AI/ML, quantum algorithms, scientific machine learning, and hardware-software co-design. You will identify where quantum computation can provide genuine structural advantage for AI workloads, develop new theoretical and empirical frameworks, and translate research insights into systems that inform real quantum hardware and AI architecture decisions.
What You'll Work On
Frontier AI for Scientific Discovery
Develop and study models for high-dimensional scientific prediction, generation, and design, including:
  • Diffusion models, flow matching, consistency models, score-based generative models, energy-based models, latent-variable models, autoregressive models, and normalizing flows.
  • Scientific foundation models for molecules, materials, proteins, quantum systems, weather, climate, PDEs, and dynamical systems.
  • Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces.
  • Models that combine prediction, uncertainty, active learning, and closed-loop design for scientific discovery.

Learning, Inference, and Reasoning
Build algorithms and theory for the computational primitives that matter most for next-generation AI systems:
  • Probabilistic inference, Bayesian modeling, variational inference, Monte Carlo methods, simulation-based inference, uncertainty quantification, and calibration.
  • Optimization, sampling, amortized inference, sequential decision-making, Bayesian experimental design, reinforcement learning, planning, and control.
  • Scientific reasoning systems, model-guided discovery, algorithmic discovery, and agents that can propose, test, and refine hypotheses.
  • Benchmarking frameworks that reveal when a new computational substrate changes scaling behavior, not just constant factors.

Quantum Algorithms for AI Workloads
Identify where quantum computation can accelerate or reshape AI-relevant subroutines, including:
  • Quantum algorithms for sampling, integration, Monte Carlo acceleration, linear algebra, optimization, Hamiltonian simulation, quantum simulation, and tensor-structured computation.
  • Fault-tolerant quantum algorithms, resource estimation, complexity analysis, block encoding, QSVT, LCU methods, amplitude estimation, phase estimation, and quantum walks.
  • Hybrid quantum-classical workflows where quantum primitives are embedded inside classical AI pipelines.
  • New quantum-native model classes, kernels, embeddings, generative processes, and inference procedures that are mathematically motivated rather than benchmark-driven alone.

Hardware-Software Co-Design
Collaborate closely with quantum architecture, systems, and hardware teams to connect AI workloads to real machine requirements:
  • Translate AI and scientific-computing bottlenecks into quantum resource requirements.
  • Design benchmarks that compare quantum, classical, and hybrid approaches under realistic assumptions.
  • Inform architecture choices by identifying the algorithms, error budgets, and primitives that matter for future AI workloads.
  • Build prototypes in Python/JAX/PyTorch and, when useful, quantum software frameworks such as PennyLane, Qiskit, Cirq, CUDA-Q, TensorCircuit, or custom simulators.
You May Be a Good Fit If You
  • Have a research record in machine learning, AI, statistics, physics, applied mathematics, computer science, quantum information, or a related field.
  • Have deep expertise in at least two of the following: generative modeling, probabilistic inference, uncertainty quantification, geometric deep learning, graph neural networks, optimization, reinforcement learning/control, numerical methods, scientific machine learning, quantum algorithms, or quantum information.
  • Have published research relevant to audiences at NeurIPS, ICML, ICLR, AISTATS, UAI, COLT, QIP, TQC, PRX Quantum, Nature, Science, or similar.
  • Can move between theory and implementation: deriving algorithms, building prototypes, running careful experiments, and communicating results clearly.
  • Are experienced with ML frameworks (PyTorch, JAX) and efficient inference implementation.
  • Are excited to work with quantum hardware teams and help define what AI workloads should demand from future fault-tolerant quantum systems.
  • Communicate complex ideas clearly across research communities
  • Value rigor: you are comfortable asking where quantum computation can help, where it cannot, and what evidence would distinguish the two.
Strong Candidates May Have
  • Research experience in diffusion/flow models, energy-based models, probabilistic programming, Bayesian deep learning, neural SDEs/ODEs, simulation-based inference, or scalable Monte Carlo.
  • Experience with AI for science: molecular design, protein design, drug discovery, materials discovery, weather or climate prediction, quantum chemistry, PDE modeling, dynamical systems, robotics, or control.
  • Experience with graph/geometric learning, equivariant architectures, neural operators, tensor networks, manifold methods, or structured world models.
  • Background in quantum algorithms, computational complexity, quantum simulation, quantum chemistry, fault tolerance, resource estimation, or quantum information theory.
  • Experience with JAX, PyTorch, CUDA/Triton, distributed training/inference, differentiable simulation, or high-performance scientific computing.
  • A track record of publishing, open-source software, or building research systems that influenced a field.

How We're Different
At Sygaldry, curiosity and intellectual courage drive our work. We approach ambitious challenges with a grounded, optimistic, and rigorous culture and know that kind people build the strongest teams. We prioritize mission over ego and collaborate openly with a strong sense of shared purpose. We dream big, yet we execute with a love of detail. We're looking for scientists, engineers, and operators to forge new paths with us at the intersection of quantum and AI.
Culture & Benefits
  • Visa Sponsorship - We know what it takes to make top talent thrive here. We're open to supporting visas whenever possible.
  • Compensation - We value your contribution and invest in your future with a competitive salary and meaningful equity.
  • Benefits - Your well-being matters. We provide company-sponsored health coverage to give you and your family peace of mind.
  • Connection - Whether it's company offsite or casual crew socials, we make time to connect, recharge, and have fun together.
  • Time Off - We trust you to take the time you need. Unlimited PTO so you can rest, recharge, and come back ready to make an impact.

We encourage applications from candidates with diverse backgrounds. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
We encourage you to apply even if you do not believe you meet every single qualification. If you don't think this role is right for you, but you believe that you would have something meaningful to contribute to our mission, please reach out at letsbuild@sygaldry.com

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