"A Product Manager's Guide to Working with Engineers: Understanding ML Algorithms and Maximizing Collaboration"

Aviral Vaid

Aviral Vaid

Jul 23, 20234 min read


"A Product Manager's Guide to Working with Engineers: Understanding ML Algorithms and Maximizing Collaboration"


Being a successful product manager involves effectively collaborating with engineers and understanding the intricacies of machine learning (ML) algorithms. This article will provide insights into both aspects and offer actionable advice for product managers to enhance their working relationships with engineers.

Building a Collaborative Relationship:

To be a multiplier of engineering resources, it is crucial for product managers to treat engineers like adults. Liz Wiseman's concept of multipliers highlights the importance of amplifying the intelligence and capabilities of those around you. Instead of solving problems for engineers, product managers should act as sounding boards, helping engineers find their own solutions. Recognizing their strengths and preferences will further multiply their impact on projects.

Incorporating Engineering Input:

A product manager's role is to find solutions that are desirable to users, viable for the business, and feasible to build within existing resources and constraints. The input of engineers on feasibility is invaluable, and it is essential to involve them early in the decision-making process. By doing so, product managers can avoid potential roadblocks and ensure that the final product aligns with technical possibilities.

Empowering Engineers:

To foster ownership and accountability, product managers can designate a lead role for engineers, whether it be as an Epic Lead, Feature Owner, or Project Owner. This individual will be responsible for delivering specific sets of tickets and managing and flagging risks and blockers proactively. Giving engineers the opportunity to take the lead not only boosts their confidence but also enhances their overall contribution to the project.

The ARISE Framework:

The ARISE framework can serve as a guide to building constructive relationships with engineers:

  • 1. Treat them like Adults: By treating engineers as equals and respecting their expertise, product managers can foster a collaborative and empowering environment.
  • 2. Recognize Different Types of Individuals: Engineers possess diverse strengths, preferences, and working styles. Understanding these differences allows product managers to tailor their approach and maximize engagement and productivity.
  • 3. Involve them Early in the Thinking Process: Seeking input from engineers at the early stages of product development ensures that technical considerations are integrated from the outset. This collaborative approach minimizes rework and improves overall efficiency.
  • 4. Give them Space to Progress: Empowering engineers to take ownership and make decisions fosters professional growth and development. Product managers should provide space for engineers to explore innovative solutions and take calculated risks.
  • 5. Invest in Good Engineering Managers: Effective engineering managers play a crucial role in bridging the gap between product managers and engineers. Investing in their development and providing them with the necessary resources and support will yield long-term benefits for the entire team.

Understanding ML Algorithms:

Machine learning algorithms are at the core of many modern products and services. To navigate this landscape effectively, product managers should grasp the fundamentals of ML algorithms.

ML algorithms aim to find patterns in data and learn from them to produce desired outputs. It is important to note that ML models are not 100% correct but rather "best guesses" based on the data they have been trained on.

Neural networks, a type of ML algorithm, consist of an input layer, an output layer, and one or more hidden layers. The hidden layers perform the computational work, while the input and output layers facilitate data ingestion and result communication, respectively.

Key Metrics for ML Algorithms:

When evaluating ML algorithms, several metrics provide insights into their performance:

  • 1. Precision: This metric measures the share of true positive predictions out of all the positive predictions generated by the algorithm. It represents the percentage of positive predictions that are correct, indicating the algorithm's accuracy in identifying positives.
  • 2. Recall: Recall represents the share of positive predictions out of all the true positives in the data. It indicates the algorithm's ability to identify true positives. Higher recall results in fewer false negatives.
  • 3. Accuracy: Accuracy is a suitable metric when the distribution of possible outcomes is uniform, and the importance of false positives and false negatives is roughly equal. However, in most cases, the distribution is not uniform, making accuracy an inadequate metric for evaluation.


To excel as a product manager, it is essential to establish productive relationships with engineers and gain a comprehensive understanding of ML algorithms. By treating engineers as equals, involving them early, and empowering them to lead, product managers can harness their full potential. Additionally, grasping the fundamentals of ML algorithms and utilizing appropriate metrics will enable product managers to make informed decisions and drive successful product development. Remember, collaboration and continuous learning are the keys to success in the rapidly evolving tech industry.

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