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Senior Machine Learning Engineer

About Us

Imagine writing software with no debugger, no logging, and only getting to compile it three times before you need to ship millions of copies. That's what building electronics products is like for hardware, quality, and process engineers. As a result of this data- and insight-poor environment, companies waste significant time, money, and material. Instrumental's manufacturing AI and data platform solves this problem for the most admired hardware companies in the world. 


Instrumental’s mission is to drive manufacturing efficiencies through technology that empowers teams to build products better, faster, and with less waste. Brands like Meta, Bose, Cisco, and SolarEdge use our technology to optimize their manufacturing processes and deliver superior products in less time. We start by collecting data that no one else can; then we use machine learning and visualizations to automatically identify defects and help companies understand their assembly processes. As a result, product and process problems are caught and solved earlier, before they create delays and cost millions.


We are ~70 people – a small but mighty team. We value an inclusive environment and actively work to promote diversity in our team. If you are looking for a place to collaborate with a mission-oriented team to deliver an outsized impact on the manufacturing industry, we’d love to hear from you!


About The Role

We’re seeking a highly customer-centric Senior ML Engineer who will join our cross-functional engineering team. You’ll be responsible for working with our talented engineers to build and maintain a highly scalable end-to-end ML production pipeline.  The role is focused on delivering production-grade ML solutions, including DL- and LLM-based approaches, that reliably work at scale.


What You'll Do
  • Maintain an obsessive focus on delivering value to our customers. 
  • Maintain ownership of large-scale ML systems, all the way to surfacing the features to customers,  and measuring their impact.
  • Partner closely with the entire R&D organization, working in a highly collaborative, cross-functional environment where you’ll be exposed to the entire scope of a deliverable rather than just the ML portion of the project. 
  • Quickly prototype and iterate on new algorithmic concepts, and prioritize them based on customer needs. Utilize state-of-the-art AI approaches, including emerging LLM-based and multimodal solutions.
  • Guide efforts to acquire high-quality datasets.
  • Manage and improve the ML pipeline, from data management, model management, and resource scheduling.


What You'll Need To Be Successful
  • Proven track record of delivering systems at scale in a production environment.
  • At least 3 years of delivering production systems in Python, Java or Scala. Familiarity with best practices for code quality, testing, and version control.
  • Start-up or equivalent experience where you demonstrate strong attention to detail and ownership balanced with a scrappy, get-stuff-done, mentality.
  • Experience with deep learning in a production setting, understanding how to manage data, training, deployment, and inference at scale with familiarity in LLM-based or computer vision-based approaches.
  • Solid understanding of data management, model deployment, and performance optimization.
  • Feel at home communicating research and other complex ideas to a broad swath of the company including engineers and non-engineers.


We’re a growing team that works collaboratively, is supportive of each other, and is highly energized by the opportunity for a large impact. We actively work to promote an inclusive environment, valuing passion and the ability to learn. You’re encouraged to apply even if your experience doesn’t precisely match the job description!


The following is a representative annual base salary range for this position within the Bay Area: $183,000-200,000. Job level and salary opportunities are evaluated through our interview process – we review the experience, knowledge, skills, and abilities of each applicant.


Instrumental is proud to offer a highly-rated variety of benefits, including health, vision, dental, commuter plans, and parental leave. 

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$183000K
$200000K

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What You Should Know About Senior Machine Learning Engineer, Instrumental

At Instrumental, we are on a mission to revolutionize the manufacturing process, and we're looking for a Senior Machine Learning Engineer to join our dynamic team in Palo Alto, CA. Imagine crafting ML solutions that help the world's leading hardware companies, such as Meta and Cisco, enhance their manufacturing efficiencies. As a Senior ML Engineer, your role will involve building and maintaining a scalable end-to-end ML production pipeline while ensuring that we deliver exceptional value to our customers. You'll collaborate with a dedicated group of engineers and other departments to develop production-grade machine learning solutions utilizing cutting-edge AI technologies including deep learning and large language models. You won’t just be coding; you’ll be part of the entire project lifecycle, guiding the acquisition of high-quality datasets and enhancing our machine learning infrastructure. The coolest part? You’ll see the direct impact of your work, from identifying defects in manufacturing processes to enabling quicker solutions that save companies millions in costs. If you're passionate about using technology to make a real difference and thrive in a collaborative environment, we want to hear from you. Let’s innovate manufacturing together!

Frequently Asked Questions (FAQs) for Senior Machine Learning Engineer Role at Instrumental
What responsibilities does a Senior Machine Learning Engineer at Instrumental have?

As a Senior Machine Learning Engineer at Instrumental, your primary responsibilities include building and maintaining scalable ML production pipelines, developing both deep learning and large language model-based solutions, and collaborating with cross-functional teams to understand and deliver value to our customers. You'll also manage data acquisition efforts and enhance the overall ML pipeline, ensuring that it runs efficiently and effectively at scale.

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What qualifications are required for the Senior Machine Learning Engineer position at Instrumental?

To qualify for the Senior Machine Learning Engineer role at Instrumental, you should have a proven track record of delivering production systems at scale over at least 3 years, preferably in Python, Java, or Scala. Familiarity with deep learning, model deployment, and performance optimization is essential. Experience in a start-up environment is a huge plus, along with effective communication skills to convey complex ideas across various teams.

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What kind of work environment can a Senior Machine Learning Engineer expect at Instrumental?

At Instrumental, the work environment is collaborative and inclusive, fostering teamwork across different departments. The small, yet mighty team of about 70 people values diversity and encourages an open exchange of ideas. Senior Machine Learning Engineers can expect a fast-paced, supportive, and dynamic workplace where innovation is at the forefront.

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How does Instrumental measure the impact of its ML solutions?

Instrumental measures the impact of its ML solutions by evaluating metrics directly related to customer satisfaction and operational efficiency. As a Senior Machine Learning Engineer, you'll play a key role in tracking these metrics, showcasing how the solutions contribute to defect identification and faster product development timelines, thereby saving our clients significant resources.

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What is the salary range for a Senior Machine Learning Engineer in Palo Alto at Instrumental?

The annual base salary range for a Senior Machine Learning Engineer at Instrumental in Palo Alto is between $183,000 and $200,000. Salary evaluations are based on experience, knowledge, skills, and abilities, and opportunities for growth are reviewed during the interview process.

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

In your response, detail specific frameworks you've worked with, such as TensorFlow or PyTorch, and describe how you've used them in past projects. Mention any challenges faced and how you overcame them, demonstrating your problem-solving skills and technical proficiency.

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How do you approach optimizing machine learning models?

Discuss your methodology for model optimization, including techniques and tools you use. Highlight your experience with parameter tuning, feature selection, and the importance of validation techniques to ensure a robust model deployment.

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Tell me about a time when you had to work with cross-functional teams.

Share an example where you effectively collaborated with engineers and non-engineers on a project. Explain how you navigated communication barriers and integrated diverse perspectives to achieve a common goal, showcasing your teamwork and leadership skills.

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What strategies do you use for managing large datasets?

Outline your techniques for data management, including data cleaning, preprocessing, and storage solutions. Emphasize your experience in acquiring high-quality datasets and how these practices contribute to the success of your machine learning projects.

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Can you explain a project where you implemented deep learning techniques?

Provide an overview of a specific project involving deep learning, detailing the problem, solution, and outcome. Focus on the impact your work had, how you utilized different deep learning architectures, and any lessons learned through the process.

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How do you ensure code quality and maintainability in your projects?

Discuss best practices you've followed regarding testing, version control, and documentation. Emphasize how maintaining high code quality is crucial for collaboration and scaling projects over time, resonating with the values at Instrumental.

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What experience do you have with deploying machine learning models?

Describe your experience in deploying models into production, including tools and strategies you've used. Talk about any issues you've encountered during deployment and how you resolved them, illustrating your adaptability in dynamic environments.

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How do you stay updated with the latest trends in machine learning?

Share specific resources you follow, such as journals, blogs, or conferences. Highlight your enthusiasm for continuous learning and how staying informed has positively impacted your work, particularly in crafting innovative solutions at Instrumental.

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What challenges do you think the manufacturing industry faces regarding machine learning?

Discuss your insights on the unique challenges such as data scarcity, model interpretability, and real-time processing in the manufacturing sector. Emphasize your ability to think critically about these issues and how you would approach solutions.

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Why do you want to work at Instrumental as a Senior Machine Learning Engineer?

Express your excitement for Instrumental’s mission and culture. Share how your skills and experiences align with their goals of leveraging AI for manufacturing efficiency, and your desire to be part of a team that prioritizes innovation and positive impact.

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To empower engineering and manufacturing teams to solve problems faster with the right data, at the right resolution, in real time.#buildbetter

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BENEFITS & PERKS
Dental Insurance
Vision Insurance
Paid Holidays
SENIORITY LEVEL REQUIREMENT
TEAM SIZE
EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
January 5, 2025

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