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Research Engineer, Algorithms
Normal Computing · New York City, New York, USA
Pay
$200k–400k
Setting
Hybrid
NORMAL COMPUTING | BUILD WITH US
Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, San Francisco, London, Copenhagen, and Pangyo.
THE ROLE
You will develop the computational methods that make AI inference run efficiently on Normal's thermodynamic hardware. The core challenge is not adapting standard GPU kernels to a new chip. It is rethinking how operations like attention, memory access, and long-context decoding behave when the underlying substrate uses stochastic analog computation in memory rather than conventional digital logic.
Normal's ASICs run the heaviest operations of large model inference inside memory itself. Your job is to develop the algorithms that exploit this natively: understand what transformer and diffusion workloads are well-suited to stochastic analog execution, design numerical methods that map onto the hardware's physical dynamics, and validate them against real silicon or high-fidelity simulation.
This is a co-design role. The hardware and the algorithms are developed in parallel, which means you will influence architectural decisions, not just implement against a fixed specification. The strongest candidates have a deep understanding of both large model inference and the mathematics of stochastic systems, and have built systems that run on real hardware, not just in theory.
WHAT YOU WILL OWN
- Algorithm Development: Develop algorithms for transformer inference workloads running on stochastic analog processing-with-memory hardware.
- Software/Hardware Co-Design: Work directly with hardware and architecture teams to shape what the chip can and should compute natively.
- Numerical Methods: Design numerical methods that exploit thermal noise and analog dynamics rather than working around them.
- Evaluation & Benchmarks: Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation.
- Workload Translation: Translate insights about model workloads into constraints and opportunities for hardware design.
- Rapid Prototyping: Prototype and iterate rapidly as hardware evolves from simulation to silicon.
- Optimizing Performance: at the gate level and the algorithmic level. and algorithms (expand/review), reinforcement learning tools.
WHAT MAKES YOU A GREAT FIT
- Deep understanding of large model inference: attention mechanisms, KV cache, long-context decoding, memory bandwidth constraints
- Experience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient attention
- Familiarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation
- Experience implementing algorithms close to hardware, not just in high-level frameworks
- Comfort reasoning from first principles about what a novel substrate can do efficiently
- Track record of taking ideas from theory to working implementation on real hardware
- Strong programming skills in Python and at least one systems language
- Collaborative instinct and ability to work across hardware, architecture, and software teams
BONUS POINTS
- PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field
- Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures
- Experience working on hardware that did not yet exist when you joined
- Publications or open-source work in efficient inference, stochastic algorithms, or novel computing
Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.
Listed by Normal Computing for a position based in the United States. Employers on this board attest they are hiring domestically.
Research Engineer, Domain Scaling
Normal Computing · New York City, New York, USA
Pay
$200k–400k
Setting
Hybrid
NORMAL COMPUTING | BUILD WITH US
Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul.
THE ROLE
The Domain Scaling team has the goal of making Normal’s Agents world-class at anything Chip-Engineering and EDA-related, UVM, debugging, analog, lean formalization, materials-aware optimization, etc. This is a unique role that combines executing directly on applied research and data sourcing (real-world and synthetic) to improve our models.
You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.
WHAT YOU WILL OWN
- Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training
- Build and manage relationships with external vendors, including outreach, evaluation of data quality, and reward design
- Collaborate with domain experts to design data pipelines and evaluations
- Explore novel ways of creating RL environments for high-value tasks
- Develop and improve QA frameworks to catch reward hacking and ensure environment quality
- Run generalization experiments to measure how data strategy changes improve model capabilities
- Partner with other AI researchers and product teams to translate capability goals into training environments, evals, and real product features
WHAT MAKES YOU A GREAT FIT
- Have experience with post-training large language models for specific domains or real-world use cases
- Have experience with reinforcement learning, reward design, or training data curation for LLMs
- Are comfortable managing technical vendor relationships and iterating quickly on feedback
- Find value in reading through datasets to understand them and spot issues
- Have strong cross-functional collaboration skills
- Are passionate about making AI more useful for chip development and recursive hardware self-improvement
- Are excited about a role that includes a combination of applied research and hands-on data work
BONUS POINTS
- Have experience training production ML systems
- Have experience designing evals or benchmarks for LLMs
- Have domain expertise in a vertical where we would like to make our models more useful
- Have experience working with external vendors or technical partners
Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.
Listed by Normal Computing for a position based in the United States. Employers on this board attest they are hiring domestically.
AI Research Engineer
Normal Computing · New York City, New York, USA
Pay
$200k–400k
Setting
Hybrid
NORMAL COMPUTING | BUILD WITH US
Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, San Francisco, London, Copenhagen, and Pangyo.
THE ROLE
We’re hiring an AI Research Engineer to push the frontier of agentic LLMs and reinforcement learning for our agentic code generation tool. You’ll design and run experiments, build agents, curate datasets from complex technical documents (e.g., chip specifications), and create rigorous evaluations. You’ll write production‑quality research code and work closely with engineering to ship improvements to customers. Leadership not required—impact through research and building is.
WHAT YOU WILL OWN
- Design and implement multi‑agent and RL approaches for agentic code generation and tool‑use.
- Build research prototypes that integrate with our agentic code generation tool; collaborate to productionize wins.
- Create evaluation suites: task specs, pass/fail checkers, coverage, cost/latency dashboards.
- Acquire and curate datasets from PDFs/logs/tables; generate synthetic data where appropriate; maintain data cards and licensing.
- Analyze experiments with disciplined ablations; document results and decisions.
- Stay current on LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, and program synthesis.
WHAT MAKES YOU A GREAT FIT
- PhD in CS/AI/ML (or equivalent research experience) with publications ideally in multi‑agent RL, agentic AI, or RL for language/code.
- Strong Python and ML framework experience (PyTorch preferred; JAX/HF a plus).
- Demonstrated ability to turn research into working systems; reproducibility mindset (tests, seeds, configs, logging).
- Experience designing eval harnesses and success metrics for sequential/agentic tasks.
- Comfortable with data acquisition/curation from documents/logs; good instincts about data quality and licenses.
- Clear communicator who partners well with engineers.
BONUS POINTS
- Research on program synthesis/codegen, constrained decoding, or execution‑based rewards.
- Experience with offline RL from tool traces or human corrections.
- Open‑source contributions (e.g., CleanRL, RLlib, AutoGen, LangGraph, CrewAI, Transformers).
- Familiarity with semiconductor/chip domains or other complex technical specs.
- Track record of shipping research to production and measuring impact.
Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.
Listed by Normal Computing for a position based in the United States. Employers on this board attest they are hiring domestically.
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