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machine learning intern

Tacit

<div>
 
 About Tacit
 
 
 
 We are an early-stage, deep tech startup based in San Francisco, developing innovative hardware that rethinks human-computer interaction. We are backed by General Catalyst, Khosla Ventures, and Greylock Partners, with a founding team from Stanford, BrainGate, Oculus, and Tesla. While we can&rsquo;t reveal too much just yet, our team is tackling cutting-edge engineering challenges to bring revolutionary products to life.
 
 
 
 About The Role
 
 
 
 As a
 
 data scientist intern
 
 , you be joining the ML / research team to develop new tools and techniques for data processing, data analysis, and improving our technology stack. You&rsquo;ll be responsible for designing, running and analyzing experiments using our system, interpreting results to inform future data collection and hardware design, and improving our decoding algorithms. This role will feature hands-on work with our basic signals coming off custom hardware all the way up to studying performance scaling across large numbers of people.
 
 
 
 What You'll Do
 
 
 
 <ul>
 <li>
 Design, implement, and test algorithms for data feature extraction from bio-signals for use in machine learning models.
 </li>
 <li>
 Rapid iteration on experiments for evaluating new hardware prototypes, data collection improvements, and to gain understanding of key aspects of our system.
 </li>
 <li>
 Develop analytical tools for visualization and establish data quality metrics.
 </li>
 <li>
 Work with hardware and electrical engineers to improve the overall sensing stack.
 </li>
 <li>
 Collaborate with ML team on data augmentation, multimodal training, synthetic data experiments for improved generalization
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 Currently enrolled in (or recently graduated from) a PhD program in neuroscience, biomedical engineering, computer science, applied math, or related fields.
 </li>
 <li>
 Effective communication and writing skills.
 </li>
 <li>
 Ability to collaborate effectively across different teams and rapidly adapt to new fields.
 </li>
 <li>
 Fluency in Python (and PyTorch is a plus).
 </li>
 <li>
 Experience with dimensionality reduction techniques and time series data analysis.
 </li>
 <li>
 Independent work ethic, flexibility, and resourcefulness.
 </li>
 <li>
 Enjoy fast moving startup environment, excited to build independently.
 
 
 </li>
 </ul>
 
 Strong candidates may have
 
 
 
 <ul>
 <li>
 Familiarity with real-time human-machine interaction, ranging from automatic speech recognition to closed-loop neural interfaces.
 </li>
 <li>
 Expertise with speech motor control, communication prostheses, and/or speech neuroscience.
 </li>
 <li>
 Prior experience with consumer wearable devices.
 </li>
 <li>
 Basic fundamentals in ML, ML infrastructure: experiment management tracking (e.g. Hydra, WandB) and AWS, Slurm.
 
 
 </li>
 </ul>
 
 Compensation Range
 
 
 
 Hourly determined based on your specific skills and experience
 
 
 
 Details
 
 
 
 <ul>
 <li>
 Company size: 20-30 people
 </li>
 <li>
 Duration: 12 weeks (Start Summer/Fall)
 </li>
 <li>
 This position is full time, on-site in San Francisco
 
 
 </li>
 </ul>
</div>

Data Scientist Intern

Rocket Lawyer

<div><div><div>About Rocket Lawyer</div>
<div></div>
<div>We believe everyone deserves access to affordable and simple legal services. Founded in 2008, Rocket Lawyer is the largest and most widely used online legal service platform in the world. With offices in North America, South America, and Europe, Rocket Lawyer has helped over 30 million people create over 50 million legal documents, and get their legal questions answered.</div>
<div></div>
<div>We are in a unique position to enhance and expand the Rocket Lawyer platform to a scale never seen before in the company's history, to capture audiences worldwide. We are expanding our team to take on this challenge!</div></div>
About your role

Rocket Lawyer is looking for a motivated and passionate AI/ML Software Engineering Intern to join our team working on Rocket Copilot, our AI assistant launched in December 2023. In this internship, you will collaborate with our cross-functional teams to assist in building a cutting edge AI platform. You'll have the opportunity to learn from experienced engineers while working on real-world AI/ML projects, helping design, implement, test, and maintain new AI features.

We value a collaborative and supportive work environment, where we celebrate learning and growth. This is a great opportunity for someone eager to expand their skills and contribute to innovative AI solutions.

Responsibilities
<ul>
<li>Assist in prompting, evaluating, fine-tuning, and maintaining large language models (LLMs) for AI features</li>
<li>Support the creation, monitoring, and troubleshooting of backend API services that interact with ML models</li>
<li>Collaborate with cross-functional teams to integrate AI into existing and new Rocket Lawyer products</li>
<li>Research and stay up-to-date with industry trends around generative AI and machine learning</li>
<li>Prototype product features to explore new AI capabilities and write clean, understandable code</li>
</ul>
What you'll need
<ul>
<li>Currently pursuing a BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a related field.</li>
<li>Basic understanding of machine learning concepts and algorithms.</li>
<li>Familiarity with generative AI technologies and models (Deep Learning, RAG, Neural Networks, Agents).</li>
<li>Interest or experience working with large language models (LLMs) and generative AI.</li>
<li>Strong programming skills in Python</li>
</ul><h3 class="jobSectionHeader">A bonus if you have</h3>
<ul><li>Experience with backend Python frameworks like FastAPI, Flask, Django, etc.</li>
<li>Familiarity with vector databases.</li>
<li>Additional programming skills in languages such as Java, Kotlin, or NodeJS.</li>
<li>SQL knowledge.</li>
<li>Interest in building user-facing products.</li>
<li>Understanding of microservices architectures and distributed systems.</li>
</ul>
Interview Process
<ul>
<li>Recruiter Phone Screen</li>
<li>Hiring Manager Interview</li>
<li>Team Interview</li>
</ul><div><div><div><div><div><div>Rocket Lawyer is proudly committed to recruiting and retaining a diverse and inclusive workforce. As an Equal Opportunity Employer, we never discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, military or veteran status, status as an individual with a disability, or other applicable legally protected characteristics. We particularly welcome applications from veterans and military spouses.</div>
<div></div>
<div>All your information will be kept confidential according to EEO guidelines.
</div><div>You may request reasonable accommodations by sending an email to hr@rocketlawyer.com.</div>
<div></div>
<div>Location: Remote, but you must be located in CA, AZ, CO, NC, or UT during the internship. 
Duration: June 8, 2026 - August 14, 2026, working 40 hours per week</div>
<div></div>
<div><div>Compensation per hour by location:</div>
<ul><li>San Francisco Bay Area, CA: $50.00</li>
<li>California (outside of San Francisco Bay Area): $46.25</li>
<li>Colorado: $42.50</li>
<li>Utah, Arizona, and North Carolina: $40.00</li></ul> 
</div>
</div></div></div></div></div><div></div><div>By applying for this position, your data will be processed as per Rocket Lawyer Privacy Policy.</div></div>

AI/ML Intern

Encord

<div>
 
 About Us
 
 
 
 At Encord, we're building the AI infrastructure of the future. The biggest challenge AI companies face today is not half as glamorous as the outside world may think: it's all about data quality. In fact, the success of any AI application today relies on the quality of a model's training data &mdash; and for 95% of teams, this essential step is both the most costly, and the most time-consuming, in getting their product to market.
 
 
 As ex-computer scientists, physicists, and quants, we felt first-hand how the lack of tools to prepare quality training data was impeding the progress of building AI. AI today is what the early days of computing or the internet were like, where the potential of the technology is clear, but the tools and processes surrounding it are still primitive, preventing the next generation of applications. This is why we started Encord.
 
 
 We are a talented and ambitious team of 90+, working at the cutting edge of computer vision and deep learning, backed by top investors, including CRV and Y Combinator, leading industry executives like Luc Vincent, former VP of AI at Meta, and other top Bay Area leaders in AI. We are one the fastest growing companies in our space, and consistently rated as the best tool in the market by our customers. We have big plans ahead and are looking for a Machine Learning Engineer to join us our ML team.
 
 
 
 
 The Role
 
 
 
 
 We Are Looking For An Experienced Machine Learning Engineer To Help Us Conduct Research On The State Of The Art Of Computer Vision And Solve Multifaceted Algorithmic Problems. You Will
 
 
 <ul>
 <li>
 Experiment with and adapt latest ML technologies to fit into existing tech stack
 </li>
 <li>
 Solve idiosyncratic statistical, geometric, and engineering problems
 </li>
 <li>
 Work closely with a full stack tech team to assist implementation of research solutions into the product
 </li>
 <li>
 Contribute to hiring additional talent to our rapidly growing team
 
 
 </li>
 </ul>
 The role will be exposed to a broad tech stack (e.g. ReactJS, Python, REST &amp; GraphQL, OpenCV, PyTorch, GCP, AWS &amp; CUDA, Kubernetes) and the cutting edge of computer vision and deep learning.
 
 
 
 Qualifications
 
 
 
 The right candidate will have a proven track record of relevant publications and previous experience managing applied research teams. Requirements for the role include:
 
 
 <ul>
 <li>
 Passion for solving ML problems
 </li>
 <li>
 Strong experience in Python and machine learning libraries such as OpenCV, PyTorch, TensorFlow, Fast.ai , and Keras
 </li>
 <li>
 Strong experience in mathematical programming, algorithmic problem solving, and applied machine learning
 
 
 </li>
 </ul>
 
 
 What We Offer
 
 
 
 
 <ul>
 <li>
 Competitive salary and equity in a hyper growth startup.
 </li>
 <li>
 Real opportunities to grow. We&rsquo;re growing insanely fast and you&rsquo;ll have all the opportunities for growth that you can handle.
 </li>
 <li>
 Strong in person culture. We're 3-5 days a week in our newly-launched loft office in North Beach.
 </li>
 <li>
 Flexible PTO to recharge.
 </li>
 <li>
 Annual learning and development budget.
 </li>
 <li>
 18 paid vacation days + federal holidays.
 </li>
 <li>
 Lots of opportunities for travel (all around the US, to London &amp; Europe).
 </li>
 <li>
 Bi-annual off-sites and monthly socials.
 </li>
 <li>
 Health, dental and vision insurance.
 
 
 </li>
 </ul>
 Encord offers a unique opportunity to be part of a startup with a clear mission and vision. You will get to explore and build services enterprise AI use cases across many different industry verticals such as healthcare, surveillance, retail, agriculture, and many more.
 
 
 Our work is at the cutting edge of computer vision and deep learning, which also includes working on solving unsolved problems within those fields.
 
 
 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
</div>

Machine Learning Engineer, Physical AI

PRAVAH

<div><h2 class="jobSectionHeader">Staff Machine Learning Engineer, Computer Vision</h2><h3 class="jobSectionHeader"> 

About Pravah</h3>
Pravah is building foundational intelligence for the electric grid. We apply modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems.
Our work sits at the intersection of computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. We leverage multimodal data - including satellite imagery, LiDAR, and street-level data - to build high-fidelity representations of grid assets and their surroundings.
We are backed by Khosla Ventures, Pear VC, and Conviction. 
 
To know more about who we are, what we are building, and why we are excited read this Notion! 
https://pravah.notion.site/<h3 class="jobSectionHeader">
The Role</h3>
We are hiring a Staff Machine Learning Engineer (Computer Vision) to lead the development of core perception and mapping systems for electric grid infrastructure.
This is a high-ownership, high-ambiguity role focused on building systems that operate on large-scale, heterogeneous visual data. You will define technical direction, make key architectural decisions, and ship models that are deployed in production.
Beyond core CV work, you will explore how state-of-the-art generative and vision architectures (e.g., ViTs, diffusion, flow matching) can be adapted to adjacent domains such as weather and spatiotemporal modeling.
There is also an opportunity to contribute to frontier work suitable for publication, particularly in areas where existing methods do not translate cleanly to real-world physical systems.<h3 class="jobSectionHeader">
What You&rsquo;ll Work On</h3>
Grid Mapping &amp; Infrastructure Understanding<ul><li>
Build and deploy models for object detection, segmentation, and instance-level understanding of grid assets and surroundings</li><li>
Work across satellite, aerial, street view, and LiDAR data to create unified representations of physical infrastructure</li><li>
Develop systems for depth estimation and spatial reasoning in complex real-world environments</li></ul> 
Multimodal &amp; Foundation Models<ul><li>
Adapt and fine-tune vision transformers and related architectures for domain-specific tasks</li><li>
Build multimodal systems that combine visual, spatial, and structured data 
Design representations that generalize across geographies, data sources, and operating conditions</li></ul> 
Applied Research &amp; New Directions<ul><li>
Bring state-of-the-art CV methods into production, bridging research and real-world deployment</li><li>
Explore the use of modern generative and vision architectures in weather and geospatial modeling applications</li><li>
Identify, prototype, and validate new approaches for modeling physical systems from visual data</li></ul><h3 class="jobSectionHeader"> 
Who You Are</h3><ul><li>
6+ years of experience building and deploying machine learning systems, with a focus on computer vision</li><li>
Strong expertise in modern deep learning approaches, including:<ul><li>
Object detection and segmentation</li><li>
Vision transformers and/or generative modeling approaches</li><li>
Multimodal learning</li></ul></li><li>
Proven track record of taking systems from research to production at scale</li><li>
Strong engineering fundamentals and proficiency in Python and ML frameworks (e.g., PyTorch)</li><li>
Experience working with large-scale, real-world datasets (e.g., geospatial, satellite, LiDAR) is a strong plus</li><li>
Comfortable operating in ambiguous environments, reasoning from first principles, and driving technical direction with high ownership</li><li>
Demonstrated ability to produce high-quality technical work, whether through systems, research, or publications</li></ul><h3 class="jobSectionHeader">
What You&rsquo;ll Gain</h3><ul><li>
Ownership of core perception and mapping systems deployed in real-world grid operations</li><li>
Opportunity to work on hard, open-ended problems at the intersection of AI and physical infrastructure</li><li>
Ability to shape technical direction and contribute to frontier ML work</li><li>
Close collaboration with a deeply technical founding team</li></ul><h3 class="jobSectionHeader">
Why This Role</h3>
This role sits at the frontier of applying modern computer vision to large-scale physical systems. Many of the problems we work on do not have established benchmarks or standard solutions. You will operate in settings where data is heterogeneous, ground truth is incomplete, and progress requires both technical depth and first-principles thinking.
Compensation Range: $150K - $200K</div>

Staff Machine Learning Engineer (Computer Vision)

LATENT

<div><h1 class="jobSectionHeader">Machine Learning Engineer</h1><h2 class="jobSectionHeader">
About Latent Health</h2>
Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family.
For everyone else, care is fragmented and impersonal.
Medical history is scattered across systems that don&rsquo;t communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly&mdash;but not the individual.
We believe this can be fundamentally rebuilt.
At Latent Health, we are building systems that understand both:<ul><li>
the population (clinical knowledge at scale)</li><li> 
and the individual (longitudinal patient history)</li></ul> 
Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning.
Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth.<h2 class="jobSectionHeader">
ML at Latent Health</h2>
The Machine Learning team is responsible for building systems that run in real clinical workflows.
We work on:<ul><li>
Verifiable reinforcement learning at scale</li><li> 
Mid-training and post-training of foundation models</li><li> 
Novel objectives derived from longitudinal patient data</li></ul> 
We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production.
We are a small team and expect engineers to take ownership of critical systems, not components.<h2 class="jobSectionHeader">
The Role</h2>
As a Machine Learning Engineer, you will own the design, development, and operation of production-grade ML systems that run in real clinical workflows.
You will drive systems from ambiguous problem definition through to reliable production deployment, setting technical direction along the way.
We are primarily hiring for senior and staff-level engineers who are comfortable owning critical systems end-to-end.
This role involves owning systems that directly impact real patient outcomes.<h2 class="jobSectionHeader">
What You&rsquo;ll Do</h2><ul><li>
Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure</li><li> 
Train and fine-tune large language models (LLMs) for:<ul><li> 
Clinical reasoning</li><li> 
Medical question answering</li><li> 
Evidence-grounded generation</li></ul></li><li> 
Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments</li><li> 
Develop evaluation frameworks to ensure model safety and clinical validity</li><li> 
Integrate ML systems into product workflows and patient-facing applications</li><li> 
Monitor system performance in production and iterate based on real-world usage and feedback</li><li> 
Define what &ldquo;correct&rdquo; means in ambiguous clinical workflows in collaboration with engineers and clinicians</li></ul><h2 class="jobSectionHeader"> 
What We&rsquo;re Looking For</h2><ul><li>
Strong foundation in machine learning and software engineering</li><li> 
Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered</li><li> 
Experience driving ambiguous ML problems from 0 1, including problem formulation, model design, and productionization</li><li> 
Hands-on experience with PyTorch or similar frameworks</li><li> 
Ability to operate independently in high-ambiguity environments with minimal guidance</li><li> 
Strong product and engineering judgment &mdash; you know when to use ML, when not to, and how to scope problems accordingly</li><li> 
Comfort working in a fast-moving, early-stage environment</li><li> 
Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)</li></ul><h2 class="jobSectionHeader"> 
Nice to Have</h2><ul><li>
Experience deploying LLMs in production environments</li><li> 
Experience building distributed systems or large-scale data pipelines</li><li> 
Experience working with clinical, biomedical, or other regulated datasets</li></ul><h2 class="jobSectionHeader"> 
Why Join Latent Health</h2><ul><li>
Work on high-stakes problems with real impact on patient care</li><li> 
Build systems that define how AI is trusted in clinical decision-making</li><li> 
Significant ownership in a small, high-caliber team</li><li> 
Competitive compensation and meaningful equity</li></ul><h2 class="jobSectionHeader"> 
Location</h2>
We are based in San Francisco and work together in person.
We spend most of the week in the office and prioritize candidates who are excited to work this way.<h2 class="jobSectionHeader">
Compensation</h2><ul><li>
Base salary: $225,000 &ndash; $300,000+</li><li> 
Meaningful equity in an early-stage, Series A company</li></ul><h2 class="jobSectionHeader"> 
Closing</h2>
If you&rsquo;re interested in building systems that bring truly personalized healthcare to millions of patients, we&rsquo;d love to talk.</div>

Machine Learning Engineer

Planet

<div>
 
 Welcome to Planet. We believe in using space to help life on Earth.
 
 
 
 Planet designs, builds, and operates the largest constellation of imaging satellites in history. This constellation delivers an unprecedented dataset of empirical information via a revolutionary cloud-based platform to authoritative figures in commercial, environmental, and humanitarian sectors. We are both a space company and data company all rolled into one.
 
 
 Customers and users across the globe use Planet's data to develop new technologies, drive revenue, power research, and solve our world&rsquo;s toughest obstacles.
 
 
 As we control every component of hardware design, manufacturing, data processing, and software engineering, our office is a truly inspiring mix of experts from a variety of domains.
 
 
 We have a people-centric approach toward culture and community and we strive to iterate in a way that puts our team members first and prepares our company for growth. Join Planet and be a part of our mission to change the way people see the world.
 
 
 Planet is a global company with employees working remotely world wide and joining us from offices in San Francisco, Washington DC, Germany, Austria, Slovenia, and The Netherlands.
 
 
 
 About The Role
 
 
 
 Planet is in the middle of its most ambitious hiring year &mdash; onboarding hundreds of new team members across San Francisco, Washington DC, Berlin, and remote locations worldwide. Behind every hire is a set of operational workflows that need to run flawlessly: requisition approvals, interview coordination, offer processing, compliance checks, onboarding handoffs, and program management for specialized hiring tracks like our global intern program. The Talent Operations team is responsible for all of it &mdash; and we are looking for a Talent Operations Partner to step in and own critical pieces of that work.
 
 
 As Talent Operations Partner, you will serve two primary functions. First, you will own and manage Planet&rsquo;s intern and early talent program end-to-end &mdash; from requisition management and candidate coordination through onboarding, program programming, and offboarding. This is a visible, cross-functional program that touches hiring managers across every department and gives early-career candidates their first experience of Planet. Second, you will contribute directly to the Talent Operations roadmap, a portfolio of process improvement and systems projects that are actively reshaping how Planet manages the hiring lifecycle.
 
 
 This role sits at the intersection of program management, process design, and recruiting operations. You will need to be equally comfortable running the logistics of an intern cohort and digging into ATS workflow configuration. The common thread is operational excellence &mdash; you care about whether things run smoothly, you notice when they don&rsquo;t, and you fix them before someone has to ask.
 
 
 You will report to the Senior Manager, Talent Operations and work as part of a team that includes Talent Operations, Recruiting Coordinators, and a Working Student. Together, this team ensures that Planet&rsquo;s recruiting infrastructure scales in step with the company&rsquo;s growth. This is a full-time, hybrid role which will require you to work from our San Francisco office 3 days per week.
 
 
 
 Impact You&rsquo;ll Own
 
 
 
 <ul>
 <li>
 Own and manage Planet&rsquo;s intern and early talent program end-to-end &mdash; including requisition intake, candidate coordination, offer processing, onboarding logistics, program events and content, manager enablement, and offboarding workflows.
 </li>
 <li>
 Serve as the primary point of contact for hiring managers and interns throughout the program lifecycle &mdash; ensuring hiring managers know what to expect and interns have a structured, high-quality experience from day one through final presentations.
 </li>
 <li>
 Partner with Talent Acquisition Partners to coordinate intern recruiting timelines, track pipeline progress across departments, and ensure all intern offers are processed accurately and on time.
 </li>
 <li>
 Contribute to the Talent Operations roadmap by owning or supporting key projects, with an immediate focus on the headcount approval workflow redesign &mdash; documenting the current process, mapping the ideal state, building automated workflows, and creating stakeholder-facing documentation.
 </li>
 <li>
 Maintain and improve ATS (Greenhouse) workflows and configurations related to intern hiring, offer approvals, and operational reporting &mdash; ensuring data integrity and process consistency across all hiring tracks.
 </li>
 <li>
 Support SLA monitoring and compliance by tracking scorecard submission timelines, offer approval turnaround, and other operational metrics &mdash; flagging risks early and following up with stakeholders to keep processes on track.
 </li>
 <li>
 Create and maintain process documentation in the talent operations knowledge base &mdash; ensuring that intern program playbooks, approval workflows, and operational procedures are current, clear, and accessible to the recruiting team and hiring managers.
 </li>
 <li>
 Coordinate cross-functionally with People Operations, People Business Partners, and the L&amp;D team on intern onboarding, compliance requirements, and program content that connects interns to Planet&rsquo;s mission and culture.
 </li>
 <li>
 Support additional recruiting operations projects as capacity allows &mdash; including AI tool deployment, global process standardization, interview process documentation, and reporting enhancements.
 </li>
 <li>
 Identify operational inefficiencies and propose improvements.
 
 
 </li>
 </ul>
 
 What You Bring
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s Degree
 </li>
 <li>
 2+ years of professional experience in recruiting operations, talent operations, HR operations, or program coordination at a technology company or similarly dynamic environment.
 </li>
 <li>
 Experience managing or supporting an intern, early talent, or campus recruiting program &mdash; including the logistics, stakeholder communication, and coordination required to run a multi-department cohort program smoothly.
 </li>
 <li>
 Familiarity with applicant tracking systems, including data entry, workflow navigation, reporting, and basic configuration.
 </li>
 <li>
 Effective project management skills: you can manage multiple concurrent workstreams, track deliverables and deadlines, and communicate progress to stakeholders without being micromanaged.
 </li>
 <li>
 Effective organizational skills and attention to detail &mdash; recruiting operations is a high-volume, high-precision function where small errors in offer letters, approval chains, or compliance documentation create real downstream problems.
 </li>
 <li>
 Clear written and verbal communication skills &mdash; you will draft process documentation, communicate with hiring managers across the company, and correspond with intern candidates who are forming their first impression of Planet.
 </li>
 <li>
 A process improvement mindset &mdash; you don&rsquo;t just run the process, you question whether it&rsquo;s the right process. You look for bottlenecks, redundancies, and manual steps that could be automated or eliminated.
 </li>
 <li>
 Comfort working in a team that is actively building and improving its own infrastructure &mdash; the Talent Operations function is evolving, and you need to be energized by contributing to that build rather than expecting a fully mature playbook.
 
 
 </li>
 </ul>
 
 What Makes You Stand Out
 
 
 
 <ul>
 <li>
 Experience with headcount planning or approval workflow tools and an understanding of how requisition approval processes connect to HRIS and finance systems.
 </li>
 <li>
 Previous experience building or redesigning an operational process from scratch &mdash; documenting current state, proposing improvements, and implementing changes with measurable results.
 </li>
 <li>
 Familiarity with intern program best practices including cohort programming, manager enablement, structured feedback cycles, and conversion-to-hire tracking.
 </li>
 <li>
 Experience working with global hiring operations, particularly coordinating intern or early talent programs that span multiple offices or geographies.
 </li>
 <li>
 Exposure to recruiting metrics and reporting &mdash; you have pulled reports from an ATS, built tracking spreadsheets, or contributed to operational dashboards that inform team decision-making.
 </li>
 <li>
 Experience with Greenhouse, the applicant tracking system.
 
 
 </li>
 </ul>
 
 Application Deadline
 
 
 
 June 6, 2026 by 11:59pm PT
 
 
 
 Benefits While Working At Planet
 
 
 
 
 These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.
 
 
 
 <ul>
 <li>
 Comprehensive Medical, Dental, and Vision plans
 </li>
 <li>
 Health Savings Account (HSA) with a company contribution
 </li>
 <li>
 Generous Paid Time Off in addition to holidays and company-wide days off
 </li>
 <li>
 16 Weeks of Paid Parental Leave
 </li>
 <li>
 Wellness Program and Employee Assistance Program (EAP)
 </li>
 <li>
 Home Office Reimbursement
 </li>
 <li>
 Monthly Phone and Internet Reimbursement
 </li>
 <li>
 Tuition Reimbursement and access to LinkedIn Learning
 </li>
 <li>
 Equity
 </li>
 <li>
 Commuter Benefits (if local to an office)
 </li>
 <li>
 Volunteering Paid Time Off
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 The US base salary range for this full-time position at the commencement of employment is listed below. Additionally, this role might be eligible for discretionary short-term and long-term incentives (bonus and equity). The final salary range is determined by job related experience, skills and location. The range displays our typical hiring range for new hire salaries in US locations only. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
 
 
 San Francisco Salary Range
 
 
 $80,200&mdash;$100,200 USD
 
 
 
 San Francisco Fair Chance Ordinance
 
 
 
 Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
 
 
 
 Why We Care So Much About Belonging.
 
 
 
 We&rsquo;re dedicated to helping the whole Planet, and to do that we must strive to represent all of it within each of our offices and on all of our teams. That&rsquo;s why Planet is guided by an ultimate north star of Belonging&mdash;dreaming big as we approach our ongoing work. If this job intrigues you, but you&rsquo;re thinking you might not have all the qualifications, please... do apply! At Planet, we are looking for well-rounded people from around the world who can contribute to more ways than just what is listed in this job description. We don&rsquo;t just fill positions, we aspire to fulfill people&rsquo;s careers, most excited about folks who are motivated by our underlying humanitarian efforts. We are a few orbits around the sun before we get to where we want to be, so we hope you&rsquo;re excited to come along for the ride.
 
 
 
 EEO Statement
 
 
 
 Planet is committed to building a community where everyone belongs and we invite people from all backgrounds to apply. Planet is an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. Know Your Rights.
 
 
 
 Accommodations
 
 
 
 Planet is an inclusive community and we know that everyone has their own needs. If you have a disability or special need that requires accommodation during the hiring process, please reach out to accommodations@planet.com or contact your recruiter with your request. Your message will be confidential and we will be happy to assist you.
 
 
 
 Privacy Policy
 
 : By clicking "Apply Now" at the top of this job posting, I acknowledge that I have read the Planet Data Privacy Notice for California Staff Members and Applicants, and hereby consent to the collection, processing, use, and storage of my personal information as described therein.
 
 
 
 Privacy Policy (European Applicants):
 
 By clicking "Apply Now" at the top of this job posting, I acknowledge that I have read the Candidate Privacy Notice GDPR Planet Labs Europe, and hereby consent to the collection, processing, use, and storage of my personal information as described therein.
 
 
 
 AI in Our Interviewing Process
 
 : Planet is committed to providing an exceptional interview experience for all candidates. We are currently exploring AI interviewing technologies to better focus on candidates and less on trying to capture notes. As such, with the candidate's consent, select interviews may be recorded and include a Planet AI Notetaker for transcription and summarization purposes. Should an interview involve use of AI interview technologies, the candidate will receive notification and have the ability to opt out both in advance and/or real-time. Opting out will not affect one's candidacy.
 
 
 
 Candidate AI Policy
 
 : Planet embraces Artificial Intelligence (AI) tools, and we encourage its responsible use. We understand that candidates may use various resources, including AI tools, to
 
 prepare
 
 for interviews and assessments. However,
 
 during any live interview stage or when actively completing assessments for this position, the use of AI tools&mdash;e.g. Large Language Models (LLMs), deep fake technology, etc.&mdash;is strictly prohibited unless explicitly prompted by an interviewer or assessment instructions
 
 . If you are unsure about acceptable use, please contact your recruiter for clarification. If an AI tool or similar technology is desired as an accommodation, please contact accommodations@planet.com with your request for assistance. Your message will be confidential, and we will be happy to assist you. Violation of this policy may result in disqualification of your application.
</div>

Talent Operations Partner

Andiamo

<div>
 Data Scientist &ndash; Product &amp; AI
 
 
 
 About The Role
 
 
 
 We are looking for a
 
 Data Scientist
 
 to help solve complex, high-impact problems in product development and AI. You will work with
 
 large-scale, high-dimensional data sets
 
 , applying statistical methods, machine learning, and experimentation to guide product decisions and improve user experiences. Your insights will influence the direction of features, platforms, and strategies used by thousands of organizations.
 
 
 As a mid-level data scientist, you&rsquo;ll own projects end-to-end: defining success metrics, designing experiments, building predictive models, and communicating findings to cross-functional partners. You&rsquo;ll collaborate with product managers, engineers, and designers to turn raw data into actionable insights that drive measurable business and product outcomes.
 
 
 This role is a chance to move beyond dashboards&mdash;you&rsquo;ll uncover patterns, tell compelling stories with data, and contribute to building scalable data science practices that push the boundaries of what&rsquo;s possible.
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Apply Analytical Methods: Use statistical modeling, machine learning, and data visualization to extract insights and guide product strategy.
 </li>
 <li>
 Experiment &amp; Learn: Design, run, and analyze A/B tests and other experiments to evaluate product changes and quantify impact.
 </li>
 <li>
 Build Predictive Models: Develop models that forecast user behavior, product performance, and key metrics.
 </li>
 <li>
 Define Success Metrics: Establish clear goals and KPIs that align product teams around measurable outcomes.
 </li>
 <li>
 Collaborate Across Teams: Partner with engineers, designers, and product leaders to ensure insights are translated into impactful features.
 </li>
 <li>
 Communicate Findings: Present results and recommendations to stakeholders, framing technical insights in clear, actionable terms.
 </li>
 <li>
 Contribute to Data Platforms: Work with engineering partners to improve data pipelines, instrumentation, and experimentation frameworks.
 
 
 </li>
 </ul>
 What We&rsquo;re Looking For
 
 
 <ul>
 <li>
 Professional Experience: Around 5 years of applied data science experience in product analytics, business intelligence, or machine learning.
 </li>
 <li>
 Programming Skills: Proficiency in SQL and Python (or R) for data manipulation, modeling, and analysis.
 </li>
 <li>
 Experimentation: Familiarity with experimental design, causal inference, and A/B testing at scale.
 </li>
 <li>
 Statistical Foundation: Strong grounding in probability, statistics, and data-driven problem solving.
 </li>
 <li>
 Communication: Ability to clearly present technical analyses to non-technical audiences and drive alignment on decisions.
 </li>
 <li>
 Education: Bachelor&rsquo;s degree in Statistics, Mathematics, Computer Science, Engineering, or related field; Master&rsquo;s degree a plus.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience working with product teams or SaaS platforms.
 </li>
 <li>
 Knowledge of ML frameworks (scikit-learn, TensorFlow, or PyTorch) and data visualization tools (Tableau, Looker, or similar).
 </li>
 <li>
 Exposure to cloud data environments (AWS, GCP, or Azure).
 
 
 </li>
 </ul>
 ✨
 
 Why Join
 
 : This is your chance to tackle challenging data problems in AI and product development. You&rsquo;ll apply advanced methods, work with talented colleagues, and contribute to products that affect millions of users and billions of data points&mdash;all while growing your skills and career.
 
 
 
 About Andiamo
 
 
 
 Talent Partners for the AI Revolution. As a globally recognized staffing and consulting firm, we specialize in placing the top 2% of technology and go-to-market professionals with the world&rsquo;s largest and most well-known companies.
 
 
 For over 20 years, we've maintained the status of tier-one vendor for firms such as Palantir, Amazon, Fluidstack, Bloomberg, Relativity Space, Firefly, MasterCard, Visa, Two Sigma, Citadel, as well as other major financial services firms, elite hedge funds, Google-backed tech start-ups, and major software firms.
 
 
 Our talent solutions include Permanent Placement, Contract Staffing, Executive Search, and Dedicated Recruiting Services (RPO). Find out more at www.andiamogo.com
</div>

Data Scientist - Global Tech Leader

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