Machine Learning Engineer, Early Stage Project
9 hours ago
About X:
X is a moonshot factory that invents and launches technologies to make the world a better place. We're a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions of people.
Our goal:
We aim to have a 10x impact on the world's most intractable problems, not just a 10% improvement. We approach projects with the speed and ambition of a startup and the riskiness of research. As an innovation engine, X focuses on turning breakthrough-technology ideas into the foundations for large, sustainable businesses.
About the team:
We're a small, passionate, and driven team of experienced ML researchers, software engineers, and product managers on a mission to make the practice of law more efficient and accurate. Our project is focusing on de-risking our technology, refining our tech prototypes, running experiments with partners, and developing a product prototype and business plans.
About the role:
As an applied ML and data pipelines software engineer, you will own the end-to-end development of breakthrough technologies that rely heavily on large language models. You will work on prompt optimization and evaluation, pipeline implementation and scalability, and production monitoring and performance. You will have the opportunity to fine-tune SOTA models with domain-specific knowledge to improve reasoning performance.
How you will make 10X Impact:
Explore, apply, and innovate state-of-the-art LLM & machine learning techniques to solve real-world problems. Lead the design and implementation of ML data platforms and service frameworks, including labeling workflows and sampling strategies. Design and implement robust, automated, production-level software using horizontally scalable components. Work effectively with cross-functional teams of engineers, product managers, and domain experts. Provide direction and focus in areas of high ambiguity.
What you should have:
Bachelor's/Master's in Computer Science, or equivalent practical experience. 7+ years of on-the-job experience developing and training ML models for product use-cases. Proficiency in MLOps tasks such as deploying and versioning ML models in production environments and monitoring their performance. Experience doing win/loss analysis on model results and systematically finding efficient ways to boost wins and reduce losses. Experience with building data pipelines (e.g. Beam, Dataflow, Flume, Flink, Hadoop, Spark). Experience testing, maintaining, or launching software products, including experience with software design and architecture. Strong proficiency in coding with Python, Golang, or other equivalent languages. Excellent written and verbal communication skills.
It'd be great if you had these:
Experience applying LLMs for large-scale document processing, including tasks such as annotation, entity extraction, etc. Experience in ML modeling using TensorFlow, PyTorch, or Jax/Flax. Experience in Google Cloud Platform and its Vertex AI solution and capabilities. Consistent track record of delivering high-quality solutions to large, complex software problems. Prior experience with setting the technical direction for small groups of engineers. Experience working in start-up like environments.
Compensation:
The US base salary range for this full-time position is $160,000 - $210,000 + bonuses + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
Equal Opportunity Employer:
We are proud to be an equal opportunity workplace and an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
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