About the Role

As a Principal AI Research Scientist, you will spearhead research into next-generation AI models (e.g. large-scale neural networks, multimodal systems, foundation models). You will set research directions and work alongside a world-class team to invent and implement new algorithms that expand our AI's capabilities. This role is both strategic and technical – you will mentor other scientists, shape our research roadmap, and still dive into coding/prototyping to turn ideas into reality. Your work will directly influence Hushh's mission to create transformative AI.

What You'll Do

• Lead cutting-edge research projects in AI/ML, from ideation to prototyping to publication, focusing on high-impact areas (such as advanced deep learning architectures, unsupervised/self-supervised learning, or reinforcement learning breakthroughs)

• Invent and optimize novel algorithms and model architectures to improve performance, efficiency, and capabilities of our Super Intelligence systems

• Collaborate closely with engineering teams to transition research innovations into production – you'll help bridge the gap between theory and practice, ensuring our most advanced models are deployable in real-world scenarios

• Mentor and guide a small team of researchers and engineers, fostering a culture of curiosity, rigorous experimentation, and exceptional quality of work

• Stay abreast of the latest scientific developments (papers, conferences, etc.) and forge external collaborations with academia or industry as needed to accelerate our research

Who You Are

• 10+ years of research experience in AI/ML (or equivalent expertise), with a PhD in Computer Science, Machine Learning, or related field. You have a strong publication record in top-tier conferences or journals, demonstrating exceptional contributions to the field

• Recognized expertise in one or more areas of AI (e.g. deep learning, natural language processing, computer vision, reinforcement learning). You can discuss both theoretical underpinnings and practical implementations of advanced models

• Proven ability to lead research teams or projects. You excel at setting a vision, executing with urgency, and guiding others to achieve ambitious goals. Prior experience as a team lead, principal scientist, or similar is a plus

• Strong coding and prototyping skills in languages and frameworks used in AI research (Python, C++ and PyTorch/TensorFlow, JAX, etc.). You are hands-on and able to turn mathematical ideas into working code

• Excellent communication skills – able to articulate complex ideas clearly and mentor junior researchers. You thrive on collaboration and knowledge-sharing in a multidisciplinary, diverse team

Tech Stack

Python, C++ (for high-performance computing), Deep Learning frameworks (PyTorch, TensorFlow, JAX), distributed computing platforms (MPI, CUDA for GPU programming)

Interview Process

After you submit your application (with a CV and a brief statement of your most exceptional research work), our team will review your background. Qualified candidates will be invited to an initial video call to discuss your experience and research vision. The main interview loop will include: 1) a deep-dive presentation of your previous research (with Q&A to assess your technical depth), 2) a collaborative problem-solving session with our scientists (tackling a conceptual research challenge together), and 3) leadership and culture fit discussions with Hushh Lab's leadership team.

About the Role

As an AI Research Scientist, you will conduct novel research and development on our core AI algorithms and models. This is a hands-on research role (not purely theoretical) – you'll be implementing and experimenting daily. You will work on projects like improving our deep learning models' reasoning abilities, scaling training to new heights, or enabling new modalities of intelligence. You'll collaborate with other research scientists and engineers to rapidly prototype ideas and publish findings, and ultimately integrate the best discoveries into our products.

What You'll Do

• Design and execute research experiments to advance the capabilities of our AI systems – for example, exploring improvements in model architectures, training techniques (optimization algorithms, novel loss functions), or data efficiency

• Develop prototypes and proof-of-concept implementations for new algorithms. You will write production-quality code to test your hypotheses on our cutting-edge computing infrastructure

• Analyze experimental results, iterate on ideas, and report insights to the team. You will help turn raw experiment data into actionable knowledge, whether that's through internal tech talks or external publications

• Work closely with our applied engineering team to transition successful research outputs into real-world applications. You'll ensure that new models can be deployed efficiently and reliably

• Stay up-to-date with the latest research in AI (reading papers, attending conferences) and contribute creative ideas to keep Hushh at the forefront of the field

Who You Are

• 3+ years of experience in AI/ML research (academia or industry) with deep expertise in areas such as neural network design, machine learning algorithms, or cognitive architectures. A Master's or PhD in a relevant field is strongly preferred (or equivalent research experience)

• Solid foundation in mathematics and algorithms underpinning AI (linear algebra, probability, optimization). You can understand and innovate upon complex ML concepts

• Demonstrated ability to write efficient code for machine learning. You are proficient in Python and frameworks like PyTorch or TensorFlow, and you can quickly implement research ideas and debugging experiments

• Track record of exceptional work – e.g. high-quality research publications, top competition results, or significant contributions to an AI project. You thrive on creative problem-solving and aren't afraid to venture into uncharted territory

• Excellent communication skills. You can clearly explain technical concepts and experimental results to both experts and non-experts

Tech Stack

Python (with PyTorch/TensorFlow), experience with distributed training (GPU clusters, HPC schedulers), data science tools (NumPy, pandas), and version control (Git)

Interview Process

Following your application (including your CV and a short description of a research project you're most proud of), we will conduct a screening interview to learn about your background and research interests. The next steps include: a technical deep-dive interview where we discuss a challenging problem in AI and assess your approach, a coding exercise (implementing a simplified version of an algorithm or model), and a presentation of prior work.

About the Role

As an AI Alignment Research Scientist, you will focus on the cutting-edge of AI safety research, helping ensure our Super Intelligence is robust, controllable, and aligned with human ethical principles. You will study and mitigate issues like model bias, unintended behavior, interpretability, and decision-making transparency. This role involves both theoretical work and practical experimentation – you might be proving theoretical guarantees one day and designing novel safety evaluation frameworks or user feedback loops the next.

What You'll Do

• Conduct research on AI alignment and safety techniques, such as reinforcement learning from human feedback (RLHF), adversarial robustness, interpretability methods, and fail-safe mechanisms for advanced AI

• Develop evaluation metrics and stress tests to probe our AI models for biases, value misalignment, or unsafe behaviors

• Collaborate with our core AI research and engineering teams to embed safety into the model development pipeline

• Stay informed about AI ethics guidelines, regulations, and societal impacts

• Publish and present important research findings in the AI safety/alignment community

Who You Are

• PhD or equivalent research experience in a relevant field (Machine Learning, Computer Science, Statistics, or even cross-disciplinary areas like AI Ethics, Cognitive Science)

• Deep knowledge of AI safety challenges and methodologies

• Solid programming and prototyping ability (Python preferred)

• Analytical and creative mindset: You excel at thinking about edge cases and 'unknown unknowns'

• Strong communication skills and ethical reasoning

Tech Stack

Python, ML frameworks (PyTorch, TensorFlow), data analysis (NumPy/Pandas, Jupyter notebooks)

Interview Process

The interview process begins with a review of your research portfolio or publications in AI safety/alignment. Selected candidates will have an initial interview to discuss your experience and motivation in this field. Subsequent rounds will include: a research presentation, a technical discussion with our AI researchers, and a culture/values interview.

About the Role

As a Machine Learning Engineer, you will design, implement, and optimize the pipelines that power our AI models in production. This is a highly impactful role: you'll be integrating our 'Super ' intelligence into various products and ensuring it performs reliably for end-users. From developing training workflows for large datasets to deploying model inference services that handle millions of requests, you will be at the core of bringing AI research to life.

What You'll Do

• Build and maintain machine learning pipelines – including data preprocessing, model training, hyperparameter tuning, evaluation, and deployment

• Develop robust production-grade AI services

• Implement integration of our core AI intelligence into products

• Optimize performance at every step: profile model bottlenecks (CPU/GPU usage, memory) and collaborate on improvements

• Continuously improve our ML engineering practices

Who You Are

• 5+ years of software engineering experience with at least 3 years focused on machine learning or AI systems in production

• Strong programming skills in Python (for ML) and ideally one of: C++/Java/Go for systems programming

• Hands-on experience with ML frameworks (TensorFlow, PyTorch, or similar) and building end-to-end ML pipelines

• Familiarity with distributed computing and cloud services

• Problem-solving mindset with a focus on impact and ownership

Tech Stack

Python (Pandas, NumPy, scikit-learn), PyTorch/TensorFlow, Docker & Kubernetes, AWS or GCP, Terraform, CI/CD pipelines

Interview Process

Our interview process involves an initial coding interview, a ML system design interview, a hands-on programming exercise focused on machine learning, and finally, you'll meet with the team for a culture fit conversation.

About the Role

As an AI Data Engineer, you will own the systems that collect, process, and manage the vast datasets used to train and evaluate our AI models. This role is about building scalable infrastructure for data – from web-scale data acquisition to efficient ETL processes and databases that can handle petabytes. You'll ensure our researchers and models always have access to high-quality data when they need it.

What You'll Do

• Design and implement massive-scale data pipelines

• Develop and maintain our data lake and feature store

• Implement data quality monitoring and validation

• Collaborate with ML researchers to understand their data requirements

• Optimize data pipelines for performance and cost

Who You Are

• 4+ years of experience as a Data Engineer or in a similar role building large-scale data infrastructure

• Strong programming skills in Python and/or Scala/Java

• Hands-on experience with cloud-based data ecosystems (AWS, GCP, or Azure)

• Familiarity with database systems – both NoSQL and SQL

• Detail-oriented and reliable: You take pride in building data systems that just work

Tech Stack

Apache Spark, Hadoop ecosystem, Python (with PySpark/Pandas), Airflow, AWS data services (S3, Athena, EMR, Kinesis)

Interview Process

Data Engineering candidates will start with a technical screen involving some SQL and coding problems focused on data manipulation. The next step is a system design interview, followed by a hands-on exercise with a sample dataset.

About the Role

As an AI Infrastructure Engineer, you will design, build, and maintain the large-scale distributed systems that underpin all of Hushh's AI efforts. Think of enormous GPU clusters, high-performance storage, and lightning-fast networks – you will be making sure all of it runs smoothly and efficiently. This role involves low-level optimization, systems engineering, and close collaboration with our ML engineers.

What You'll Do

• Architect and manage our HPC cluster for AI research

• Optimize system performance end-to-end

• Oversee our high-speed data storage and pipelines

• Develop automation and monitoring tools

• Collaborate closely with AI engineers to support new needs

Who You Are

• 5+ years of experience in infrastructure, systems engineering, or devops/SRE

• Deep knowledge of Linux systems administration and performance tuning

• Experience with cluster management for compute-intensive workloads

• Strong programming/scripting ability in Bash and at least one language like Python or Go

• Familiarity with GPU computing and distributed ML training is a big plus

Tech Stack

Linux (Ubuntu) servers, Kubernetes, Terraform/Ansible, monitoring with Prometheus/Grafana, networking hardware (InfiniBand, high-speed Ethernet)

Interview Process

The interview process will assess both your systems knowledge and your problem-solving approach. It starts with a technical screening, then a deep-dive interview with our current infrastructure engineers, followed by a hands-on practical exercise.

About the Role

As a Site Reliability Engineer, you will be responsible for the reliability and smooth operation of Hushh's AI platforms and services. You'll work on everything from monitoring the health of deployed AI APIs to refining our CI/CD pipelines and emergency response playbooks. This role is equal parts systems engineer, software developer, and firefighter.

What You'll Do

• Implement and enhance monitoring, alerting, and incident response for our AI services

• Develop automation scripts and tools to eliminate manual work

• Performance tuning and capacity planning

• Collaborate on CI/CD pipelines and release processes

• Work with security engineers to ensure our deployments and operations are secure

Who You Are

• 3-6 years experience as an SRE, DevOps Engineer, or similar role managing large-scale online services

• Proficient in at least one programming or scripting language (Python, Go, Bash, etc.)

• Hands-on experience with monitoring and observability stacks

• Experience with containerization and orchestration (Docker, Kubernetes)

• Familiar with CI/CD tools and best practices

Tech Stack

Kubernetes on cloud, Terraform, Jenkins, Prometheus + Grafana, Python/Go microservices

Interview Process

The interview process includes a technical screening with a focus on troubleshooting and Linux fundamentals, then a coding/automation exercise, followed by an on-call simulation and culture fit interview.

About the Role

As a Full-Stack AI Product Engineer, you will develop end-to-end features for applications that showcase our Super Intelligence. One week, you might be building a slick web interface that allows users to interact with our AI agent; the next, you might be optimizing a backend service to handle complex queries efficiently. You will take ownership of product features from concept to deployment.

What You'll Do

• Develop user-facing web applications that seamlessly integrate AI functionalities

• Build and maintain backend APIs and services that power these applications

• Collaborate with UI/UX designers to implement features that are not only functional but also delightful to use

• Optimize performance on both front-end and back-end

• Work closely with the AI team to understand the outputs and constraints of our models

Who You Are

• 4+ years of experience in full-stack or software engineering roles

• Strong proficiency in frontend technologies: HTML5, CSS3, and JavaScript/TypeScript

• Solid experience in backend development: designing APIs and building services

• Familiarity with integrating AI/ML models or APIs into applications is a big plus

• Product-minded: you think from the end-user's perspective

Tech Stack

Frontend uses React + TypeScript, with Tailwind CSS, Backend is Node.js/TypeScript with Express and Python microservices, PostgreSQL and Redis, GCP with Kubernetes

Interview Process

The interview process will cover both frontend and backend skills. Expect a coding exercise, a system design interview, and a session with our designers or product managers to discuss how you approach building user-centric products.

About the Role

As a Product Manager for AI-Powered Products, you will own one or more product lines that incorporate our Super Intelligence. This could range from an AI-driven knowledge assistant, to a predictive analytics tool, to a platform enabling other developers to use our AI. Your role is to define the product strategy, gather requirements, and coordinate execution across multidisciplinary teams.

What You'll Do

• Define product vision and roadmap

• Gather and prioritize requirements

• Work hand-in-hand with engineering, research, design, and data teams to execute on the roadmap

• Own the product launch and iteration cycle

• Serve as the product's chief evangelist and quality officer

Who You Are

• 5+ years of product management experience, with at least 2 years managing a technology product

• Strong technical acumen

• User-focused mindset with proven ability to translate user needs into product features

• Excellent communication and leadership skills

• Entrepreneurial and creative: You thrive in ambiguity

Tech Stack

Familiarity with agile tools (JIRA), prototyping and analytics tools (Figma, Mixpanel/Google Analytics), and basic understanding of AI/ML tooling

Interview Process

The PM interview process involves a mix of product sense evaluation, technical understanding, and leadership/collaboration assessment. You can expect a case study exercise and deep dives into your past product experience.

About the Role

As an MLOps Engineer, you will design and maintain the pipelines and tools that make our machine learning workflows reproducible, scalable, and robust. This spans everything from source data versioning, experiment tracking, to model serving infrastructure. You'll be tasked with enabling our scientists to do their work more effectively and our engineers to deploy models seamlessly.

What You'll Do

• Develop and enhance our ML pipeline orchestration

• Implement continuous integration/continuous deployment (CI/CD) for ML models

• Build and maintain an experiment tracking system

• Manage model serving infrastructure

• Work on data versioning and lineage

Who You Are

• 3+ years of experience in a DevOps/SRE or software engineering role with a focus on machine learning workflows

• Strong coding skills in Python and familiarity with one or more ML frameworks

• Experience with containerization and orchestration

• Familiarity with MLOps tools and platforms

• Knowledge of CI/CD pipelines and infrastructure-as-code

Tech Stack

Python, Docker, Kubernetes, CI/CD (Jenkins/GitHub Actions), MLFlow, Apache Airflow or Kubeflow, DVC for data versioning

Interview Process

The interview will include a coding test, a design interview where we ask you to outline an MLOps pipeline for a hypothetical scenario, and discussions about real experiences with ML pipelines you've improved.