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Remote Machine Learning Postdoc information

What is a remote machine learning postdoc?

A Remote Machine Learning Postdoc is a postdoctoral researcher specializing in machine learning who works predominantly or entirely from a location outside their host institution, often from home. Their work involves conducting advanced research, developing new algorithms, analyzing data, and publishing findings related to machine learning while collaborating virtually with faculty and research teams. This role is ideal for researchers seeking flexibility or those who cannot relocate but wish to contribute to academic or industrial research from a distance.

What are the key skills and qualifications needed to thrive as a remote machine learning postdoc?

A Remote Machine Learning Postdoc requires a PhD in computer science, statistics, or a related field, with expertise in machine learning algorithms, statistical modeling, and research methodologies. Proficiency in programming languages like Python or R, experience with machine learning frameworks such as TensorFlow or PyTorch, and familiarity with version control systems (e.g., Git) are typically necessary. Strong written and verbal communication, self-motivation, and collaboration skills are vital for remote research and effective teamwork. These capabilities enable impactful independent research, smooth collaboration across distributed teams, and the successful dissemination of findings to the wider scientific community.

What are some common challenges faced by remote machine learning postdocs when collaborating with research teams?

Remote machine learning postdocs often encounter challenges related to communication and coordination, especially when working across different time zones or with teams that have varying schedules. Effective collaboration usually requires proactive communication through virtual meetings, shared code repositories, and regular progress updates. Building rapport with colleagues and staying engaged with ongoing research discussions can take extra effort remotely, but leveraging collaborative tools and participating in virtual seminars or group chats can help bridge the gap. Being organized and self-motivated is key to ensuring productive contributions to the team’s research objectives.

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Remote Machine Learning Postdoc jobs near you

Sr. Manager, Machine Learning Engineering-Applied Research

Pinterest

San Francisco, CA • On-site, Remote

Full-time

Re-posted 2 days ago

Key responsibilities

  • Manage, inspire, and develop a team of machine learning researchers and engineers specializing in recommendation systems.

  • Own the technical roadmap and strategic vision for Pinterest's next-generation recommendation systems.

  • Collaborate with cross-functional teams to prototype, build, and scale solutions to complex engineering challenges.

Job description

Pinterest is on a mission to bring everyone the inspiration to create a life they love. The Applied Science team plays a critical role in this mission by developing cutting-edge machine learning solutions that scale across all of Pinterest engineering teams (see our team's  publications).

We're looking for a highly technical Engineering Manager with a deep understanding of modern recommendation systems to manage, lead and develop a team of machine learning researchers and engineers within the Applied Science team.  In this role, you will help the team build a portfolio of work which can balance that addresses both immediate short-term business needs and long-term strategic breakthroughs.  You will partner with senior leaders to evolve our technical roadmap and directly drive Pinterest's core mission forward. 

What you will do:

  • Vision and Strategy: Own the technical roadmap and strategic vision for Pinterest's next-generation recommendation systems. Champion the use of state-of-the-art ML techniques to deliver revolutionary innovations in recommendation technology.
  • Research to Production: Successfully transition breakthrough ML research into production-ready systems that directly impact core company metrics.
  • Team Leadership and Culture: Manage, inspire, and develop a talented team of machine learning researchers and engineers specializing in recommendation systems. Partner with your team to define their charter, ensuring a strong balance between cutting-edge research and building foundational embeddings that benefit products across the entire company.
  • Cross-functional Collaboration: Collaborate with Core Engineering, Ads Engineering, Infrastructure, Content, and Data Science teams to prototype, build, and scale solutions to complex engineering challenges. Partner with leadership to deepen user understanding and set the strategic direction for our recommendation system roadmap.

What we are looking for:

  • Technical Depth: 7+ years of combined post-graduate academic and industry experience applying state-of-the-art ML technologies to real-world problems on web-scale data, alongside 3+ years of direct people management experience.
  • Proven Execution: A track record of delivering high-impact initiatives across multiple product areas, with a demonstrated ability to influence peers and leadership using data-driven insights.
  • Talent Development: Experience mentoring, coaching, and up-leveling software and machine learning engineers.
  • Continuous Learner: A self-propelled learner who stays ahead of industry trends, new tools, and emerging methodologies, with an appetite for building proof-of-concept prototypes.
  • Business & Product Acumen: The ability to transform vague, ambiguous questions into well-defined projects with clear success metrics that drive business decisions.
  • Communication & Credibility: Excellent communication skills with the ability to distill complex technical findings for leadership and product teams, backed by a strong track record of publications in machine learning, AI, data science, or related technical fields.
  • Academic Credentials: MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field
  • Nice to have:
    • Track record of publishing at top-tier ML/RecSys conferences (KDD, RecSys, NeurIPS, CVPR).
    • Experience leveraging modern LLM/Agentic workflows and generative AI capabilities to accelerate engineering productivity and context extraction.

In-Office Requirement Statement:

  • We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
  • This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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