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Solutions Engineer

About the team

Adaptive ML is helping companies build singular generative AI experiences by democratizing the use of reinforcement learning. We are building the foundational technologies, tools, and products required for models to learn directly from user interactions and for models to self-critique and self-improve based on simple guidelines. Our founders come from diverse backgrounds and previously worked together in creating state-of-the-art open-access large language models. We closed a $20M seed with Index & ICONIQ in early 2024 and are live with our first enterprise customers.

At Adaptive ML, our Success team is part of our Technical Staff. Our Technical Staff develops the foundational technology that powers Adaptive ML, while the Success Team ensures our enterprise customers effectively leverage these technologies—particularly through our Adaptive Engine product—to unlock maximum value. 

About the role

As a Solutions Engineer at Adaptive ML, you will help our customers build scalable production applications with Adaptive Engine. Our customers range from innovative scale-ups to global enterprises, and your role will be to guide them in maximizing business value while shortening time to production. We place tremendous importance on bridging the gap from proof-of-concept to production, as too many generative AI projects fail to demonstrate tangible ROI.

By leveraging state-of-the-art reinforcement learning technology, you will support the specialization of large language models for production deployment. You will build synthetic data pipelines to bootstrap models into production and identify signals that can be used in production to enable continuous model improvement using production feedback. We are looking for self-driven, business-minded, and ambitious individuals interested in supporting real-world deployments of a highly technical product. As this is an early role, you will have the opportunity to shape our research efforts and product as we grow.

This role is ideally in-person at our Paris or New York office, but we are also open to fully remote work.

Your responsibilities

  • Embed directly with our customers, helping them maximize the value of their Adaptive Engine deployment;

  • Experiment with pipelines to personalize models at scale, leveraging synthetic data and production feedback;

  • Ensure customer success, owning problems end-to-end from ideation to deployment, validation, and production;

  • Work closely with our research staff, productionizing the latest frontier research initiatives;

  • Contribute to our product roadmap, identifying promising trends and customer needs;

  • Report clearly on your work to a distributed collaborative team, with a bias for asynchronous written communication.

Your (ideal) background

The background below is only suggestive of a few pointers we believe could be relevant. We welcome applications from candidates with diverse backgrounds; do not hesitate to get in touch if you think you could be a great fit, even if the below doesn't fully describe you.

  • A M.Sc./Ph.D. in computer science, or demonstrated experience in software engineering, preferably with a focus on machine learning;

  • 4-8+ years of technical consulting (or equivalent) experience;‍

  • Strong programming skills, with Python experience required and TypeScript experience preferred, from quick prototyping to production deployments;

  • Enthusiasm for collaborating with diverse technical teams, with a humble attitude and empathy;‍

  • Passionate about the future of generative AI, and eager to build foundational technology to help machines deliver more singular experiences.

Benefits

  • Comprehensive medical (health, dental, and vision) insurance;

  • 401(k) plan with 4% matching (or equivalent);

  • Unlimited PTO — we strongly encourage at least 5 weeks each year;

  • Mental health, wellness, and personal development stipends;

  • Visa sponsorship if you wish to relocate to New York or Paris.

Average salary estimate

$100000 / YEARLY (est.)
min
max
$80000K
$120000K

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 Solutions Engineer, Adaptive ML

Join Adaptive ML as a Solutions Engineer and become a key player in shaping the future of generative AI in New York City. Our dynamic team is dedicated to democratizing machine learning and creating singular, impactful experiences for our clients. As a Solutions Engineer, you will work directly with a diverse range of customers—from ambitious start-ups to established enterprises—helping them maximize the value of our Adaptive Engine product. Your insightful guidance will play a crucial role in shortening the time to production while ensuring meaningful business outcomes. Imagine leveraging cutting-edge reinforcement learning technology and being at the forefront of self-improving models. You'll get involved in building synthetic data pipelines and supporting continual model advancement using real-world feedback. We seek self-motivated, business-oriented individuals who are eager to thrive in this fast-paced, technical environment. You’ll have the unique opportunity to shape our research efforts and adapt our products during our exciting expansion phase. Our culture fosters open collaboration, and while we prefer in-person roles at our New York office, we are happy to consider remote options for the right candidates. If you're ready to contribute your expertise and passion for generative AI to help enterprise customers achieve tangible ROI, we'd love to hear from you!

Frequently Asked Questions (FAQs) for Solutions Engineer Role at Adaptive ML
What are the main responsibilities of a Solutions Engineer at Adaptive ML?

As a Solutions Engineer at Adaptive ML, you will embed directly with customers to help them leverage the Adaptive Engine to its fullest potential. Your responsibilities will include experimenting with pipelines, personalizing models at scale, and ensuring customer success from concept to deployment. You will also collaborate closely with our research team to implement the latest advancements and contribute to the product roadmap by identifying trends and needs.

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What qualifications are required for the Solutions Engineer role at Adaptive ML?

Candidates for the Solutions Engineer position should ideally hold an M.Sc. or Ph.D. in computer science or have equivalent experience in software engineering with a focus on machine learning. Additionally, a successful candidate should have 4-8 years of technical consulting experience, strong programming skills (especially in Python), and preferably some exposure to TypeScript.

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How does Adaptive ML support customer success for the Solutions Engineer role?

At Adaptive ML, the Solutions Engineer is pivotal in ensuring customer success throughout the deployment process. This involves owning the problem end-to-end, ensuring validation, and gathering production feedback to continuously improve the model. Your work will help customers gain true value from their Adaptive Engine deployment, which is at the heart of our mission.

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What growth opportunities exist for Solutions Engineers at Adaptive ML?

Being an early member of the technical staff, Solutions Engineers at Adaptive ML have significant opportunities for professional growth. You'll get to lead research efforts, contribute to innovative product development, and influence the technical direction of our offerings while working closely with a talented team that values your input.

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Is remote work an option for the Solutions Engineer position at Adaptive ML?

Yes, while we have a preference for in-person collaboration at our New York office, Adaptive ML is open to offering fully remote positions for the right candidates. We recognize the importance of flexibility and how it can enhance work-life balance.

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Common Interview Questions for Solutions Engineer
Can you describe a time when you improved a machine learning model in production?

When answering this question, focus on your specific actions and the methods used to track improvements. Discuss how you collected production feedback, made necessary adjustments, and what impact it had on model performance. Emphasize your problem-solving skills and technical approach.

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What experience do you have with synthetic data generation?

Explain any projects where you've constructed synthetic data pipelines, detailing the tools and techniques you used. Highlight your ability to closely collaborate with cross-functional teams to ensure that the synthetic data meets production standards.

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How do you ensure successful collaboration with customers while managing technical projects?

Share strategies you employ for effective communication and managing expectations. Highlight your approach to building relationships, listening to customer needs, and providing solutions that align with their business goals.

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Describe your experience with reinforcement learning technologies.

Provide a brief overview of your previous work with reinforcement learning, including specific applications and any models built. Be sure to communicate your passion for the technology and your understanding of its future potential in generative AI.

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What tools and programming languages are you proficient in?

List the programming languages you are skilled in, particularly Python and any relevant ML frameworks you’ve worked with. Mention your experience with TypeScript if applicable, as well as any tools that relate to data analysis and modeling.

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How do you approach problem-solving in a technical environment?

Discuss your methodical approach to problem-solving, perhaps mentioning specific frameworks or methodologies you utilize. Emphasize your analytical skills and how you apply them to dissect complex technical issues and find effective solutions.

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What are your thoughts on the future of generative AI?

Share your insight into generative AI trends, including ethical considerations and potential applications. This is a great opportunity to connect your vision with the mission of Adaptive ML, so express your eagerness to contribute to shaping this technology.

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How do you prioritize tasks when managing multiple projects?

Express your organizational strategies for task management, such as prioritization based on urgency and impact. Highlight your experience with project management tools and collaborative techniques to ensure deadlines are met without sacrificing quality.

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Can you give an example of how you handled a challenging customer interaction?

Provide an example that showcases your communication skills and empathetic approach. Discuss the steps you took to resolve the issue and how it led to positive outcomes not only for the customer but also for your team.

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What motivates you to work in the field of machine learning?

Share your personal motivations, whether they stem from a passion for technology, the desire to solve complex problems, or the opportunity to make a meaningful impact. Relate your enthusiasm for the field to the work Adaptive ML is doing, showing alignment with the company’s mission.

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Every user interaction, advancing your use case. Test, serve, monitor, and iterate on your large language models in your cloud with Adaptive ML.

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Full-time, hybrid
DATE POSTED
December 24, 2024

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