Full-Time

QA Engineer

Confirmed live in the last 24 hours

Together AI

Together AI

51-200 employees

Decentralized cloud services for AI development

Compensation Overview

$160k - $220k/yr

+ Equity + Benefits

Senior

San Francisco, CA, USA

Category
QA & Testing
Quality Assurance
Required Skills
Agile
SCRUM
Requirements
  • Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent work experience)
  • 7+ years of experience in QA engineering, with a strong background in release management and automated testing
  • Proven experience with release management processes and best practices
  • Strong communication skills and the ability to work effectively with cross-functional teams
  • Experience working with SDETs and Engineering teams to increase coverage of high priority features
  • Manage competing priorities to keep high standards of quality
  • Excellent problem-solving skills with a keen attention to detail.
Responsibilities
  • Develop and maintain release management processes, ensuring that all releases are thoroughly tested and meet quality standards before deployment
  • Identify potential risks in the release process and implement strategies to mitigate them
  • Establish and enforce QA best practices and standards for testing, automation, and documentation
  • Create detailed and comprehensive test scenarios, use cases, and documentation to guide testing efforts and ensure thorough coverage
  • Continuously evaluate and refine testing processes and methodologies to improve efficiency and effectiveness
  • Analyze test results, identify trends, and provide actionable insights to improve software quality
  • Work closely with development teams to understand new features, changes, and potential impacts on the release process
  • Provide regular updates on testing progress, release readiness, and quality metrics to stakeholders
  • Participate in Agile/Scrum ceremonies and contribute to sprint planning and retrospectives.

Together AI focuses on enhancing artificial intelligence through open-source contributions. The company offers decentralized cloud services that allow developers and researchers to train, fine-tune, and deploy generative AI models. Their services cater to a wide range of clients, including small startups, large enterprises, and academic institutions. Together AI's business model is based on providing cloud-based solutions, generating revenue through service subscriptions and usage fees. What sets Together AI apart from its competitors is its strong commitment to open and transparent AI systems, which aims to foster innovation and achieve beneficial outcomes for society.

Company Size

51-200

Company Stage

Series B

Total Funding

$533.5M

Headquarters

Menlo Park, California

Founded

2022

Simplify Jobs

Simplify's Take

What believers are saying

  • Together AI secured $305M to expand its AI Acceleration Cloud with NVIDIA GPUs.
  • The rise of open-source AI models creates collaboration opportunities for Together AI.
  • Smaller, efficient AI models like DeepCoder-14B offer cost-effective client solutions.

What critics are saying

  • Emergence of competitors like Deep Cogito challenges Together AI's market position.
  • Snowflake's AI hub could divert talent and resources from Together AI.
  • Continuous innovation pressure from models like DeepCoder-14B highlights competitive challenges.

What makes Together AI unique

  • Together AI focuses on open-source contributions, fostering community-driven AI innovation.
  • The company offers decentralized cloud services for AI model training and deployment.
  • Together AI's business model includes service subscriptions and usage fees for cloud solutions.

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Benefits

Health Insurance

Company Equity

Growth & Insights and Company News

Headcount

6 month growth

-4%

1 year growth

6%

2 year growth

-1%
VentureBeat
Apr 23rd, 2025
More Accurate Coding: Researchers Adapt Sequential Monte Carlo For Ai-Generated Code

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Coding with the help of AI models continues to gain popularity, but many have highlighted issues that arise when developers rely on coding assistants. However, researchers from MIT, McGill University, ETH Zurich, Johns Hopkins University, Yale and the Mila-Quebec Artificial Intelligence Institute have developed a new method for ensuring that AI-generated codes are more accurate and useful. This method spans various programming languages and instructs the large language model (LLM) to adhere to the rules of each language.The group found that by adapting new sampling methods, AI models can be guided to follow programming language rules and even enhance the performance of small language models (SLMs), which are typically used for code generation, surpassing that of large language models.In the paper, the researchers used Sequential Monte Carlo (SMC) to “tackle a number of challenging semantic parsing problems, guiding generation with incremental static and dynamic analysis.” Sequential Monte Carlo refers to a family of algorithms that help figure out solutions to filtering problems. João Loula, co-lead writer of the paper, said in an interview with MIT’s campus paper that the method “could improve programming assistants, AI-powered data analysis and scientific discovery tools.” It can also cut compute costs and be more efficient than reranking methods. The researchers noted that AI-generated code can be powerful, but it can also often lead to code that disregards the semantic rules of programming languages. Other methods to prevent this can distort models or are too time-consuming. Their method makes the LLM adhere to programming language rules by discarding code outputs that may not work early in the process and “allocate efforts towards outputs that more most likely to be valid and accurate.”Adapting SMC to code generationThe researchers developed an architecture that brings SMC to code generation “under diverse syntactic and semantic constraints.” “Unlike many previous frameworks for constrained decoding, our algorithm can integrate constraints that cannot be incrementally evaluated over the entire token vocabulary, as well as constraints that can only be evaluated at irregular intervals during generation,” the researchers said in the paper. Key features of adapting SMC sampling to model generation include proposal distribution where the token-by-token sampling is guided by cheap constraints, important weights that correct for biases and resampling which reallocates compute effort towards partial generations.The researchers noted that while SMC can guide models towards more correct and useful code, they acknowledged that the method may have some problems.“While importance sampling addresses several shortcomings of local decoding, it too suffers from a major weakness: weight corrections and expensive potentials are not integrated until after a complete sequence has been generated from the proposal. This is even though critical information about whether a sequence can satisfy a constraint is often available much earlier and can be used to avoid large amounts of unnecessary computation,” they said. Model testingTo prove their theory, Loula and his team ran experiments to see if using SMC to engineer more accurate code works. These experiments were:

VentureBeat
Apr 10th, 2025
Deepcoder Delivers Top Coding Performance In Efficient 14B Open Model

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Researchers at Together AI and Agentica have released DeepCoder-14B, a new coding model that delivers impressive performance comparable to leading proprietary models like OpenAI’s o3-mini. Built on top of DeepSeek-R1, this model gives more flexibility to integrate high-performance code generation and reasoning capabilities into real-world applications. Importantly, the teams have fully open-sourced the model, its training data, code, logs and system optimizations, which can help researchers improve their work and accelerate progress.Competitive coding capabilities in a smaller packageThe research team’s experiments show that DeepCoder-14B performs strongly across several challenging coding benchmarks, including LiveCodeBench (LCB), Codeforces and HumanEval+.“Our model demonstrates strong performance across all coding benchmarks… comparable to the performance of o3-mini (low) and o1,” the researchers write in a blog post that describes the model.Interestingly, despite being trained primarily on coding tasks, the model shows improved mathematical reasoning, scoring 73.8% on the AIME 2024 benchmark, a 4.1% improvement over its base model (DeepSeek-R1-Distill-Qwen-14B). This suggests that the reasoning skills developed through RL on code can be generalized effectively to other domains.Credit: Together AIThe most striking aspect is achieving this level of performance with only 14 billion parameters. This makes DeepCoder significantly smaller and potentially more efficient to run than many frontier models.Innovations driving DeepCoder’s performanceWhile developing the model, the researchers solved some of the key challenges in training coding models using reinforcement learning (RL).The first challenge was curating the training data

VentureBeat
Apr 8th, 2025
New Open Source Ai Company Deep Cogito Releases First Models And They’Re Already Topping The Charts

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PR Newswire
Mar 18th, 2025
Weka Expands Nvidia Integrations And Certifications, Unveils Augmented Memory Grid At Gtc 2025

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