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Machine Learning Research Engineer

About Prima Mente

Prima Mente’s goal is to deeply understand the brain, to protect the brain from neurological disease and enhance the brain in health. We do this by generating our own data, building brain foundation models, and translating discovery to real clinical and research impact.

Role focus - Foundation Models for Biology

You will play a pivotal role in designing, implementing, and scaling foundational AI models and infrastructure for multi-omics at massive scale. Your work will directly drive breakthroughs in scientific understanding and contribute to transformative applications in medicine and biology.

  • Implement high-performance ML algorithms optimised for massive-scale, ensuring reliability, efficiency, and scalability.

  • Design, develop, and maintain robust experimentation pipelines enabling rapid iteration, precise evaluations, and reproducible research outcomes

  • Refactor and scale prototype research code into clean, maintainable, and performant repositories suitable for production-grade deployments.

  • Create high-speed data processing workflows capable of efficiently handling large-scale datasets to accelerate experimentation and model development.

  • Experimental design, prioritising high impact experiments with the highest signal:noise ratio.

Expected Growth 

  • In 1 month you will be responsible for running initial experiments with state-of-the-art machine learning models, reviewing and implementing cutting-edge research papers, and optimizing existing code for efficiency and accuracy.

  • In 3 months you will directly own and have created a prototype model architecture, demonstrated significant algorithmic improvements, and contributed to scaling methods for large-scale data ingestion and training.

  • In 6 months, you’ll have developed a high-performance version of a foundation model, implemented key algorithmic optimizations that boost scalability and throughput, and published internal benchmarks demonstrating significant research impact.

Why Join Us:

  • Meaningful Impact: Contribute directly to research infrastructure that powers discoveries potentially impacting millions of lives.

  • Innovation & Autonomy: Work at the forefront of AI and multi-omics, with the freedom to propose and implement state-of-the-art infrastructure solutions.

  • Exceptional Team: Collaborate with talented colleagues from diverse backgrounds across ML, bioinformatics, and engineering.

  • Growth Opportunities: Continuous learning and growth opportunities in a rapidly advancing technical field.

Culture Insight 

What we are doing is extremely hard. Prima Mente is for great people. We are team players who appreciate challenges, want to be hands-on, and thrive on curiosity by throwing away assumptions. We are focused on excellence at pace and huge personal growth. We are strong communicators who are highly disciplined and rigorous.

Prima Mente operates with a flat organizational structure. We gain and share knowledge by contributing to multiple opportunities. Leadership is given to those who show initiative and consistently deliver excellence. 

We arrange our lives so we can work in person as much as possible. 

Our Values 

  • Exceptional performance at exceptional pace

    • The solutions we build demand uncompromising quality and rigour.

    • The problems we are solving are grave and present.

  • Inquisitive discovery

    • We embrace curiosity and creativity.

    • Every question is a path to a transformational breakthrough.

  • Radical candour

    • We practice unwavering honesty and transparency in all our challenges and interactions.

  • Purposeful individuality

    • Every individual in our team is celebrated for their identity, uniqueness, and experiences.

    • We are invested in each one’s bespoke personal development.

    • Nurturing individuality will supercharge our collective purpose and spirit.

  • Patient impact at scale

    • We have a steadfast commitment to improve the health and well-being of patients globally.

    • Every experiment run, every dataset analysed, and every innovation developed, is a step towards achieving a scalable impact.

Who You Are

  • Ambitious and Impact-Driven: You're inspired by working at the forefront of AI and biology, motivated by challenges that can significantly advance human health.

  • Technical Excellence: You thrive in highly technical, complex environments and have a track record of turning cutting-edge research into robust production systems.

  • Collaborative & Communicative: You excel at collaborating across disciplines, clearly articulating complex ideas, and driving alignment among research and engineering teams.

Ideal experience 

  • Deep understanding of state-of-the-art machine learning methodologies and proven experience in translating them into practical solutions.

  • Solid foundation in distributed computing principles, parallel processing, and algorithmic efficiency.

  • Experience optimizing ML algorithms for performance, memory efficiency, and compute resource utilization.

  • Skilled in designing and implementing scalable data pipelines capable of rapid ingestion, transformation, and processing.

  • Deep expertise in modern ML frameworks and tools (e.g., PyTorch, JAX, TensorFlow), and familiarity with state-of-the-art training and inference workflows.

  • Skilled in clearly articulating complex ideas, effectively communicating why particular approaches succeed or fail, and providing insightful critical analyses.

  • Experience of building highly collaborative research teams. 

  • Track record of working on hard problems for long periods of time. 

  • High agency with the ability to jump on any task as needed. 

  • Demonstrated experience training, optimizing, and deploying large-scale models (7B+ parameters).

  • Low level algorithm optimisation

    • Quantization (8bit or lower)

    • JIT compilation

    • XLA/Mosaic/Triton/CUDA

  • Hardware optimisation (GPU/TPU/HPU)

  • Finetuning Optimization (QLora, QDora) 

  • Large scale data above 2T tokens

Interview Process 

Our intention is to run our interview process end to end within 2 weeks. You will interact with co-founders Ravi and Hannah, as well as every member of the technical team. 

Average salary estimate

$140000 / YEARLY (est.)
min
max
$120000K
$160000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Machine Learning Research Engineer, Prima Mente

At Prima Mente, we're on a mission to revolutionize our understanding of the brain and tackle neurological diseases through cutting-edge technology. As a Machine Learning Research Engineer, you will be at the forefront of this effort, designing and implementing essential foundational AI models and infrastructure for multi-omics on a massive scale. Imagine contributing to breakthroughs that could significantly advance human health! Your role will involve optimizing high-performance ML algorithms and setting up robust experimentation pipelines that support rapid iteration and reproducible findings. You'll get the chance to refactor prototype research code into tidy, maintainable repositories that can be deployed in production. With a focus on high-impact experiments and data processing workflows, you'll not only accelerate model development but also take pride in creating solutions that have a real-world medical impact. Plus, at Prima Mente, we're all about growth! In just a month, you’ll start running experiments and implementing cutting-edge research, and by six months, you’ll have a high-performance foundation model under your belt and a portfolio of benchmarks demonstrating significant contributions to research. Join us in a collaborative environment that values individual contributions, transparency, and innovation. If you're ambitious, driven by purpose, and excited about the crossroads of AI and biology, we encourage you to apply!

Frequently Asked Questions (FAQs) for Machine Learning Research Engineer Role at Prima Mente
What are the main responsibilities of a Machine Learning Research Engineer at Prima Mente?

As a Machine Learning Research Engineer at Prima Mente, you'll be responsible for designing, implementing, and scaling foundational AI models for multi-omics. Your duties include creating high-performance ML algorithms, developing robust experimentation pipelines, and processing vast datasets efficiently. You’ll also refactor prototype research code into production-quality systems, ensuring your work has a tangible impact on groundbreaking discoveries in medicine and biology.

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What qualifications do you need to become a Machine Learning Research Engineer at Prima Mente?

To become a Machine Learning Research Engineer at Prima Mente, you should have a deep understanding of state-of-the-art machine learning methodologies and practical experience in their implementation. A solid foundation in distributed computing principles and algorithmic optimization is essential, along with proficiency in ML frameworks like PyTorch or TensorFlow. Experience with large-scale models and collaborative research is also desirable, as well as strong communication skills to articulate complex ideas effectively.

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How does the Machine Learning Research Engineer contribute to research impact at Prima Mente?

The Machine Learning Research Engineer at Prima Mente contributes significantly to research impact by developing foundational AI models that enhance our understanding of biology. By implementing high-performance algorithms and agile experimentation pipelines, the engineer ensures rapid learning and adaptation in research processes, driving innovations that have the potential to change lives and improve health outcomes on a large scale.

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What is the growth trajectory for a Machine Learning Research Engineer at Prima Mente?

At Prima Mente, Machine Learning Research Engineers have a clear growth trajectory. In the first month, you’ll engage in initial experiments and optimize research papers. By three months, you'll be responsible for creating prototype model architectures, while within six months, you’ll contribute significantly to high-performance model development and establish internal benchmarks that showcase your impact. The supportive and collaborative culture enables continuous learning and advancement in cutting-edge fields.

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What is the work culture like for a Machine Learning Research Engineer at Prima Mente?

The work culture for a Machine Learning Research Engineer at Prima Mente is vibrant and collaborative, centered around curiosity, transparency, and exceptional performance. The flat organizational structure fosters open communication and knowledge sharing, while the emphasis on individuality ensures every team member’s unique perspectives contribute to the collective mission. This environment nurtures both personal and professional growth while striving towards impactful solutions in healthcare.

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Common Interview Questions for Machine Learning Research Engineer
Can you explain a machine learning project you've worked on and your role in it?

When answering this question, highlight a specific project that showcases your technical skills and collaborative efforts. Discuss what algorithms you used, the challenges faced, and how your contributions propelled the project forward. Be sure to detail your methodology and the impact the project had, especially in a healthcare context, to align with Prima Mente’s mission.

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How do you approach optimizing machine learning models for performance?

Discuss your methodical process for optimizing models, including techniques like fine-tuning hyperparameters, employing regularization strategies, and leveraging advanced architectures. Provide examples of successful optimizations you've implemented in previous roles, focusing on performance improvements and efficiencies gained.

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What techniques do you use to handle large-scale datasets?

Share your experience with techniques such as distributed computing, parallel processing, and the specific frameworks you prefer for handling large datasets. Mention any tools you're familiar with for data ingestion and processing, elucidating your role in managing data pipelines that effectively support large-scale model training.

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Describe how you would refactor prototype research code for production deployment.

Explain your approach to code refactoring with a focus on improving maintainability, performance, and scalability. Talk about your practices for documentation, testing, and ensuring code quality while transitioning it from a prototype to a production-ready state. Highlight your attention to detail and best practices that minimize technical debt.

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How do you stay current with advancements in machine learning and AI?

Articulate your commitment to continuous learning. Mention reading research papers, contributing to discussions in professional communities, participating in workshops, or using online courses. Discuss how these efforts help you adopt relevant innovations into your work, ensuring that your contributions to Prima Mente remain at the cutting edge of technology.

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What is your experience with collaborative research teams?

Share your experiences working within multidisciplinary teams, emphasizing your ability to communicate complex ideas clearly. Discuss how your collaborative skills have helped in aligning project goals between research and engineering teams, ensuring that everyone is on the same page and working towards shared objectives.

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How would you prioritize your experiments in a multi-omics environment?

Discuss your strategy for prioritizing experiments based on impact and signal-to-noise ratio. Explain how you leverage both preliminary results and theoretical considerations guiding your choices, with the aim of maximizing productivity and scientific gain while minimizing wasted effort.

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Can you describe your experience with state-of-the-art ML frameworks like PyTorch or TensorFlow?

Detail your hands-on experiences with ML frameworks, citing specific projects where you've utilized them effectively. Explain how you chose the appropriate framework for tasks based on their strengths, along with any notable performance improvements or results achieved through their use.

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Have you developed any high-performance models? If so, could you elaborate?

Provide a comprehensive overview of a high-performance model you’ve developed, focusing on the methodology you used, challenges encountered, and the outcomes achieved. Your response should highlight your analytical and coding skills, as well as how the model you created aligns with Prima Mente’s objectives.

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What are some key considerations when designing ML workflows?

Discuss critical aspects such as scalability, ease of reuse, and automation in ML workflows. Emphasize the importance of thorough documentation and testing to facilitate collaboration and maintain quality. Provide examples of how you've implemented robust workflows that enhanced project outcomes and team efficiency.

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MATCH
VIEW MATCH
FUNDING
SENIORITY LEVEL REQUIREMENT
TEAM SIZE
No info
HQ LOCATION
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EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
April 15, 2025

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