Staff Machine Learning Engineer Job at Inworld AI, Mountain View, CA

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  • Inworld AI
  • Mountain View, CA

Job Description

Why Join Inworld

Inworld is the leading provider of AI technology for real-time interactive experiences, with a $500 million valuation and backing from top tier investors including Intel Capital, Microsoft’s M12 fund, Lightspeed Venture Partners, Section 32, BITKRAFT Ventures, Kleiner Perkins, Founders Fund, and First Spark Ventures.

Inworld provides the market’s best framework for building production ready interactive experiences, coupled with dedicated services to optimize specific stages of development – from design and development, to ML pipeline optimization and custom compute infrastructure. We help developers bring their AI engines in-house with a framework optimized for real-time data ingestion, low latency, and massive scale. Inworld powers experiences built by Ubisoft, NVIDIA, Niantic, NetEase Games and LG, among others, and has partnerships with key industry players such as Microsoft Xbox, Epic Games, and Unity.

Inworld was recognized by CB Insights as one of the 100 most promising AI companies in the world in 2024 and was named among LinkedIn's Top Startups of 2024 in the USA.

We are seeking Staff and Principal level Machine Learning Engineers with extensive experience in researching, designing, and implementing Machine Learning systems. You will be at the forefront of building generative AI products and agentic frameworks that utilize a wide range of modern AI stacks, including LLMs, diffusion-based synthesis models, and many more.

Qualifications

  • BA/BS degree or higher in Computer Science, Engineering, or a similar technical field.
  • 6 years of experience in software development in one or more programming languages such as Python or C++.
  • 4 years of experience in applying ML algorithms in natural language processing and/or speech processing and/or action-planning domains.
  • Fluency with ML frameworks such as PyTorch, TensorFlow, or JAX.
  • Experience in fine-tuning and evaluating LLMs such as LLaMA, Mistral, Qwen, etc., is a significant plus.
  • Knowledge of working with embedded systems and/or running ML on edge devices is a big plus.

You May Be a Good Fit If You Have

  • Significant software engineering experience and a results-oriented mindset.
  • Strong problem-solving and analytical skills, with a proactive approach to tackling challenges.
  • Familiarity with modern AI tools and technologies, such as transformers, diffusion models, reinforcement learning.
  • Ability to work collaboratively in a fast-paced environment with shifting priorities.
  • Passion for learning and staying up-to-date with the latest advancements in machine learning research and its applications.

Responsibilities

  • Research and experiment with cutting-edge ML models and techniques to advance Inworld’s AI capabilities.
  • Develop production-scale infrastructure to train, evaluate, and serve ML models efficiently.
  • Provide guidance and mentorship to junior engineers, fostering a culture of learning and collaboration.

Representative Projects

  • Exploring the impact on quality and latency of using a new attention implementation algorithm within the LLM serving infrastructure.
  • Designing and implementing a high-level public API to enable developers to create new AI voices and use them at runtime.
  • Exploring various on-device matrix multiplication backends to optimize transformer-based speech recognition models for a specific device.

In-office location: Mountain View, California, United States. Candidates must be based in the SF Bay Area or willing to relocate (you will be working on-site in our South Bay office a few days a week).

The US base salary range for this full-time position is $240,000 - $385,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future.

Job Tags

Full time, Relocation, Shift work,

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