AI Research Engineer (Kernel & Inference Optimization) 100% Remote WorldwideJoin Tether and Shape the Future of Digital FinanceAt Tether, we're not just building products, we're pioneering a integral financial revolution.
Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains.
By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost.
Transparency is the bedrock of everything we do, ensuring trust in every transaction.Innovate with TetherTether Finance: Our innovative product suite features the world's most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.But that's just the beginning:Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, g among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awardsWHY JOIN USIf you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLEWe are looking for a Senior Full Stack Developer to build and maintain backend services and frontend interfaces that surface, validate, and resolve issues in data coming from ETL pipelines, data lake outputs, and ML model results for a large-scale marketing analytics platform.
You will develop REST APIs in Python using FastAPI or Flask, build responsive React and TypeScript frontends, manage PostgreSQL schemas and migrations, and leverage AI-assisted development tools with the critical judgment to push back on shortcuts that compromise long‐term maintainability.
You will work in an environment where requirements are often ad hoc and underspecified, taking ownership of defining the problem, gathering context, identifying constraints, and shaping practical solutions before implementationWHAT YOU WILL DOBuild and maintain scalable backend services (REST APIs) and responsive frontend interfaces (React/TypeScript).
Build the interfaces and services that surface, validate, and resolve issues in data coming from the data lake, ETL pipelines, health-check logic, or model outputs — without necessarily owning pipeline architecture.Package and manage applications using Docker.Use AI‐assisted development tools (e.g., Claude Code)



to move fast during implementation — while critically evaluating AI‐generated output and pushing back when it isn't the right long‐term solution.Drive conversations directly with engineers to clarify both functional and non‐functional requirements, rather than defaulting to whatever "just works" in the short term.Work daily alongside engineers over Slack and Google Meet.MUST HAVES5–7 years of full-stack software development experience .
Strong proficiency in Python (FastAPI or Flask) and TypeScript/React .
Solid understanding of relational databases (PostgreSQL) and database migrations .
Comfortable with REST APIs ; able to work with and migrate away from legacy GraphQL where needed.Demonstrated experience building or maintaining data‐heavy applications (large datasets, performance‐sensitive queries at the application layer).
Practical, hands‐on use of AI‐assisted development tools , paired with the critical judgment to challenge AI output when it compromises long‐term maintainability.High comfort level working entirely in a Mac/Linux terminal environment (Bash, Makefiles).
Strong soft skills (mandatory, not a bonus) : the ability to hold and defend a technical opinion — challenging a stakeholder's or a tool's proposed "quick fix" with sound reasoning in pursuit of a solution that scales and is maintainable long‐term, while still being pragmatic enough to ship.Comfort with ambiguity (mandatory) : work is frequently ad hoc and underspecified.
This role requires defining the problem — gathering context, identifying constraints, and framing the work — before solving it, rather than waiting for a specification.
Experience limited to well‐specified work executed through agent workflows is not a fit.NICE TO HAVESExposure to object‐storage‐backed data lakes and serverless query engines (S3 + Athena, or comparable — Trino/Presto, BigQuery, Spark SQL).
Familiarity with DAG‐based task orchestration (Airflow, Dagster, Prefect, Step Functions), or the ability to pick it up quickly.Data engineering exposure — experience building or maintaining ETL pipelines, advanced SQL (complex queries, data validation, optimization).
Caching/NoSQL systems (Redis, DynamoDB).
Monorepo tooling or modern package managers (Nx, Poetry, UV).




Familiarity with AWS Bedrock or similar AI/LLM SDKs.Experience with marketing data structures, campaign management APIs, or digital advertising metrics.PERKS AND BENEFITSGrowth without limits : build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated ancations.
Define clear success metrics such as improved real‐world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements.A degree in Computer Science or related field.
Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D; (with good publications in A* conferences).
Must have knowledge of Metal Shading Language (MSL).
You should be comfortable writing custom compute shaders from scratch.Proven experience in low‐level kernel optimizations and inference optimization on mobile devices is essential.
Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain‐specific applications, particularly on resource‐constrained devices and edge platforms.A deep understanding of modern model serving architectures and inference optimization techniques is required.
This includes state‐of‐the‐art methods for achieving low‐latency, high‐throughput performance, and efficient memory management in diverse, resource‐constrained deployment scenarios.Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines.
Practical experience in developing and deploying end‐to‐end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource‐constrained devices is required.Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints.
You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.Distributed Inference Systems: Designing and optimizing high‐performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters.Deep understanding of the math and structure behind Diffusion Models and Vision TransformersUnderstanding of Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc.
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📌 Remote Gpu Kernel Engineer Cuda/Triton Performance Debugging (Santa Fe)
🏢 Anyone AI
📍 Santa Fe