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Machine Learning Engineer

Paradigm is rebuilding the clinical research ecosystem by creating a platform that enables equitable access to trials for all patients while enhancing trial efficiency and reducing the barriers to participation for healthcare providers. Incubated by ARCH Venture Partners and backed by leading healthcare and life sciences investors, Paradigm aims to break down barriers across the trial ecosystem through one seamless infrastructure implemented at healthcare provider organizations, bringing potentially life-saving therapies to patients faster.

Our team is diverse in its experience and committed to the company’s mission to create equitable access to clinical trials for any patient, anywhere. Join us, and bring your expertise, passion, creativity, and drive as we work together to realize this mission.

We are looking for a Machine Learning Engineer to join our team. In this role, you will contribute to building NLP and LLM-based models to streamline trial execution activities and integrate them into provider workflows. This is an opportunity to contribute to developing innovative tools to improve protocol design, increase patient recruitment, and speed up clinical trial conduct. Your work will positively impact the lives of patients and help move the science and practice of clinical research forward.

What You'll Do:

  • Build, test, and deploy ML models and pipelines at scale, working collaboratively with other engineers and data scientists.
  • Collaborate with clinicians, informaticists, and engineers to implement solutions that align with company goals.
  • Develop and maintain GenAI/LLM-based models to streamline trial execution activities, including gathering data, training, and evaluating models.
  • Work with engineering teams to integrate models into production systems and build tools for monitoring and improving model performance.
  • Communicate technical insights and findings to both technical and non-technical stakeholders within the company.

Who You Are:

  • Masters or PhD in statistics, computer science, or a related field.
  • 2+ years of experience as a machine learning engineer or data scientist, ideally in or adjacent to the healthcare industry.
  • Hands-on experience in training, evaluating, deploying, and fine-tuning ML models.
  • Proficiency with Python, SQL and familiarity with various machine learning algorithms and statistics.
  • Experience in working in production-level engineering architecture, including pipelines and model deployment.
  • Strong problem-solving skills and ability to collaborate effectively across teams.
  • Comfortable in a fast-paced, mission-driven startup environment.

Bonus:

  • Experience working with oncology and/or clinical trials data.
  • Familiarity with developing GenAI/LLM-based models and working with open-source frameworks for LLM applications.
  • Previous experience in an early-stage startup environment is a plus.

At Paradigm, we are committed to providing equal employment opportunities to all qualified individuals. We believe in creating a diverse and inclusive workplace that values the contributions of every employee, regardless of their race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by law. We are an equal-opportunity employer and do not discriminate against any employee or applicant for employment based on any of the aforementioned factors. We encourage and welcome candidates from diverse backgrounds and perspectives to apply for our open positions. We strive to provide reasonable accommodations to qualified individuals with disabilities and to ensure that all employment decisions are based on job-related factors such as skills, experience, and qualifications.

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CEO of Paradigm
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Nathan Herbst
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Average salary estimate

$110000 / YEARLY (est.)
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$90000K
$130000K

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What You Should Know About Machine Learning Engineer, Paradigm

Paradigm is on a mission to revolutionize the clinical research ecosystem, and we're looking for a talented Machine Learning Engineer to join our remote team! You’ll play a key role in building NLP and LLM-based models that streamline trial execution activities, making it easier for healthcare providers and patients to access clinical trials. Our goal is to enhance trial efficiency and reduce barriers for participation, and your work will directly contribute to this mission. Imagine being part of a diverse team dedicated to improving patients' lives through innovative tools that optimize protocol design and boost patient recruitment. You’ll be involved in building, testing, and deploying machine learning models and pipelines at scale, collaborating closely with engineers and data scientists, as well as clinicians and informaticists. You will develop and maintain GenAI/LLM models, integrate these models into production systems, and create effective monitoring tools to enhance model performance. Great communication skills are necessary as you’ll need to convey technical insights to both technical and non-technical stakeholders within the company. If you have a Master's or PhD in statistics, computer science, or a related field, along with 2+ years of experience as a Machine Learning Engineer in the healthcare industry, we want you to bring your expertise and creativity to Paradigm. This is not just a job; it's an opportunity to contribute meaningfully to something bigger—helping bring life-saving therapies to patients faster. Join us at Paradigm, where we’re focused on creating equitable access to clinical trials for every patient, everywhere.

Frequently Asked Questions (FAQs) for Machine Learning Engineer Role at Paradigm
What does a Machine Learning Engineer do at Paradigm?

As a Machine Learning Engineer at Paradigm, you'll be responsible for building, testing, and deploying machine learning models, specifically focusing on NLP and LLM-based solutions. You will collaborate with other engineers and healthcare professionals to enhance trial efficiency and improve access to clinical trials.

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What qualifications are needed for the Machine Learning Engineer position at Paradigm?

Candidates for the Machine Learning Engineer role at Paradigm should have a Master's or PhD in statistics, computer science, or a related field, along with a minimum of 2 years of experience in the healthcare industry. Furthermore, proficiency in Python, SQL, and various machine learning algorithms is essential.

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What is the work environment like for a Machine Learning Engineer at Paradigm?

Paradigm offers a flexible and fast-paced remote work environment that values diversity and collaboration. As a Machine Learning Engineer, you will be part of a mission-driven team committed to making a positive impact in clinical research.

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What technologies will I work with as a Machine Learning Engineer at Paradigm?

In this role, you will primarily work with Python and SQL, and you'll be involved with developing GenAI/LLM-based models. Familiarity with machine learning frameworks and tools is also important as you'll develop and deploy models in production-level systems.

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Are there growth opportunities for Machine Learning Engineers at Paradigm?

Absolutely! Paradigm encourages continuous learning and development. As a Machine Learning Engineer, you'll have opportunities to expand your skills in innovative technology and apply them to impactful projects in clinical research.

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What is the focus of Paradigm's projects for Machine Learning Engineers?

The focus of Paradigm's projects is to develop innovative tools that streamline clinical trial processes, improve patient recruitment, and enhance protocol design. This work is essential in making clinical trials more accessible and efficient.

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How does Paradigm ensure an inclusive workplace for its Machine Learning Engineers?

Paradigm is committed to creating a diverse and inclusive workplace. The company values contributions from all employees and strives to welcome candidates from various backgrounds and perspectives, fostering an environment where everyone is valued.

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Common Interview Questions for Machine Learning Engineer
Can you describe your experience with machine learning algorithms?

In response to this question, focus on specific algorithms you have used, why you chose them for various projects, and the results obtained. Highlight your understanding of the strengths and weaknesses of these algorithms in real-world applications.

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How do you handle data preprocessing for machine learning models?

Discuss your approach to data cleaning, normalization, and transformation processes. Provide examples of techniques you’ve employed to prepare datasets for analysis, as well as any challenges faced and how they were overcome.

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What projects have you worked on that are similar to Paradigm’s focus?

Share previous projects where you applied machine learning in healthcare or clinical trials. Discuss the outcomes and how your contributions helped improve processes or access to treatment, making sure to align your experiences with Paradigm's mission.

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How do you ensure the accuracy and reliability of your machine learning models?

Explain your methods for model validation, including cross-validation techniques and performance metrics. Discuss how you monitor model performance post-deployment to ensure ongoing reliability, and share examples of adjustments made based on performance data.

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Why is collaboration important in machine learning projects?

Highlight the interdisciplinary nature of machine learning, especially in healthcare. Explain how collaborating with clinicians, informaticists, and other engineers can lead to more effective solutions and enhance the quality of the final product.

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What challenges have you faced in your machine learning career, and how did you solve them?

Discuss one or two specific challenges related to model training, data quality, or integration into systems. Describe the steps you took to address them, showcasing your problem-solving skills and adaptability.

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How do you approach feature selection and engineering?

Talk about the criteria you use for selecting features and how you determine which features to engineer. Provide examples from past projects that demonstrate your strategic approach and its impact on model performance.

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Can you explain a complex machine learning concept to a non-technical audience?

When answering, use simple analogies or relatable examples to break down the concept. Demonstrating clear communication skills is crucial in showing your ability to convey complex ideas effectively to all stakeholder levels.

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What tools and frameworks do you prefer for machine learning projects?

Mention the tools you've previously used, such as TensorFlow, PyTorch, or Scikit-learn. Discuss why you prefer certain tools based on the context of your projects, focusing on their advantages in performance, scalability, or integration.

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What trends do you see shaping the future of machine learning in healthcare?

Share your insights on emerging trends such as reinforcement learning, interpretability of models, or ethics in AI. Discuss how these trends could positively influence patient care and improve clinical research outcomes.

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Paradigm is a software development company that offers a platform that focuses on the building products industry. Their customers include renovation contractors, homebuilders, dealers, lumberyards, distributors, retailers, and manufacturers.

49 jobs
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FUNDING
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TEAM SIZE
SALARY RANGE
$90,000/yr - $130,000/yr
EMPLOYMENT TYPE
Full-time, remote
DATE POSTED
November 24, 2024

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