Top Tech Jobs & Startup Jobs

Own full-cycle recruiting for go-to-market and business teams in a high-growth AI company. Partner with executives to define hiring strategies, candidate profiles, and interview processes; manage candidates through offer close; provide market intelligence and funnel metrics; and improve recruiting workflows and candidate experience. The role requires strategic leadership partnership, adaptability in ambiguity, and experience building recruiting processes from the ground up.

Lead architecture and implementation of Together AI’s global GPU cloud infrastructure. Own GPU and network virtualization, in-data-center IaaS, distributed scheduling and management planes, fault-tolerant automation, and customer-facing production systems. Set technical direction across teams, mentor engineers, establish engineering standards, and build scalable services spanning Kubernetes, compute, storage, networking, and GPU infrastructure.

Conduct research on frontier AI agents, including alignment, reliability, long-horizon reasoning, dataset design, evaluation, safety, and scalable infrastructure. Develop novel techniques, run rigorous experiments, document findings in publications and blog posts, and communicate project progress. Work may involve machine learning, deep learning, reinforcement learning, spoken-input agents, and foundation model development.

Software Engineer Interns will design, develop, and maintain software across platform, infrastructure, inference, and product engineering teams. Responsibilities include building features, writing maintainable code, collaborating cross-functionally, participating in code reviews, debugging, and optimizing performance. The 12-week, on-site internship is based at the company headquarters in San Francisco and may involve cloud infrastructure and open-source projects.

Develop and optimize GPU-accelerated kernels and algorithms for AI and machine learning applications. Collaborate with modeling, algorithm, hardware, and software teams on GPU architecture and programming-model co-design. Integrate GPU solutions into software systems, optimize performance and scalability, and track advances in GPU programming techniques. The internship lasts 12–14 weeks and includes cross-functional research and engineering work.

Conduct frontier AI research focused on agentic systems, alignment, reliability, evaluation, datasets, self-learning, and long-horizon reasoning. Responsibilities include implementing novel techniques, designing rigorous experiments, documenting findings in publications and blog posts, and communicating project results. Work may involve training and evaluating models, developing safety paradigms, building spoken-input agents, and creating scalable ML infrastructure.

Develop automated tests for website and service functionality, performance, and reliability. Improve test automation frameworks, expand coverage, write maintainable code, and collaborate with engineering and product teams on test plans and quality standards. The intern will gain exposure to AI infrastructure, model benchmarking, API testing, and CI automation.

Develop and optimize GPU-accelerated kernels and algorithms for ML/AI applications. Collaborate with modeling, algorithm, hardware, and software teams on GPU and programming-model co-design, integrate optimized solutions, and apply performance profiling techniques to improve scalability and efficiency.

Software Engineer Interns will design, develop, and maintain software across platform engineering, infrastructure, SRE, and inference teams. Responsibilities include building product features, writing maintainable code, collaborating cross-functionally, participating in code reviews, debugging, and optimizing performance. Interns will contribute to projects from initial scope through production and may work on cloud infrastructure or open source initiatives.

Conduct research on efficient, scalable inference systems for foundation models. Design experiments, develop cross-layer optimizations across models, software, and hardware, and investigate distributed inference, speculative decoding, KV cache design, compiler optimization, and large-scale serving. Communicate project progress and results, and document findings in scientific publications and blog posts.