Senior Machine Learning Engineer - Perception

Picklerobot

Charlestown, MA

Hybrid

Who this role is best for

Computer vision experts with end-to-end ML project experience will find this perception-focused role in logistics automation highly aligned with their background and technical stack.

Best fit for

Candidates with experience in multi-task or multi-modal vision models and a history of deploying models at scale
— “fine-tune large backbones with multiple task-specific heads”
Individuals who have built and managed large-scale datasets with automated quality assurance and coverage analysis
— “Hands-on Experience Building Datasets at Scale: generation or collection, curation, automated quality assurance, coverage and bias analysis”
Candidates who can translate product goals into rigorous ML problem statements and evaluate models with precision
— “Own ML Problem Formulation: You will turn ambiguous picking performance goals into well-posed ML problems”

Things to consider

This role demands significant involvement in synthetic data generation and cloud infrastructure for training.
— “from large-scale synthetic scene generation and distributed cloud dataset jobs”
The position requires close collaboration with platform and robotics engineers, though not ownership of runtime integration.
— “partnering with the engineers who own runtime integration and inference optimization rather than owning that yourself”

How to stand out

Highlight your experience with multi-view or reward-driven learning models in your resume and interviews.
— “Professional experience with at least one of the following: ... reward-driven learning”
Emphasize your ability to define offline and online evaluation metrics that predict real-world performance.
— “Own Evaluation and Verification: You will define offline metrics that predict on-robot outcomes”
Showcase your track record of delivering measurable outcomes from ML projects, not just model development.
— “models you personally carried from problem statement to measured outcome”

Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

successful model training and deployment
improved picking performance

Typical background

5+ years of experience in machine learning and computer vision

Skills & requirements

Required

Machine LearningComputer VisionLarge Vision ModelsData Engine BuildingEvaluation And VerificationCode ReviewsPythonPyTorch

Preferred

Multi-task ModelsMulti-modal ModelsMulti-view ModelsSelf-supervised Learning

Stack & domain

Machine LearningComputer VisionLarge Vision ModelsFine-tuning Large BackbonesMultiple Task-specific HeadsDisciplined ExperimentsEvaluationPythonPyTorchCloud Training InfrastructureDockerExperiment Tracking ToolsMulti-task ModelsMulti-modal ModelsMulti-view ModelsSelf-supervised LearningOutcome-based LearningReward-driven LearningProblem-solvingTechnical Trade-offsCode ReviewsMentoringRoadmap ContributionDeployable ModelsReproducible ArtifactsRuntime IntegrationInference OptimizationPhysical AiGlobal Supply ChainsWarehouse TeamsLogistics ProcessesRoboticsGrasping PackagesPerceptionAutonomy

About the role

Original posting from Picklerobot via Lever

About Us
At Pickle Robot, we're on a mission to automate global supply chains with Physical AI. Our robots work alongside warehouse teams to unload trucks and containers — one of the toughest, most understaffed jobs in logistics — making the work safer, faster, and more efficient for the people doing it. Loading trucks comes next, followed by the Dill Autonomy Engine: generalized autonomy that will eventually orchestrate robots across entire logistics processes.
We're looking for a rigorous and self-directed Senior Machine Learning Engineer to own machine learning problems end-to-end on the systems that decide how our robots grasp packages. This role will focus on problem formulation, building the data engines that feed our models, training and experimentation, and the evaluation rigor that tells us whether a model is genuinely better. Our platform and robotics engineers own runtime integration and edge deployment, so you can stay focused on the modeling loop.
What You'll Do:
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Own ML Problem Formulation: You will turn ambiguous picking performance goals into well-posed ML problems, deciding what the model predicts, how labels and rewards are defined, what loss to optimize, and what "better" actually means. This is the core of the role.
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Build the Data Engine: You will own dataset strategy end-to-end, from large-scale synthetic scene generation and distributed cloud dataset jobs to scene diversity filtering and self-supervised label generation. You will identify when the data, rather than the model, is the bottleneck, and fix it.
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Train and Iterate on Large Vision Models: You will fine-tune large backbones with multiple task-specific heads, run disciplined experiments, and know both which experiment to run next and when to stop.
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Own Evaluation and Verification: You will define offline metrics that predict on-robot outcomes and online metrics that help you improve performance on edge cases.
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Drive Technical Excellence: As a senior member of the team, you will conduct rigorous code reviews, mentor engineers earlier in their careers, and contribute to the perception and grasping roadmap.
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Hand Off Cleanly to Production: You will produce deployable models with sane latency budgets, exportable graphs, and reproducible artifacts, partnering with the engineers who own runtime integration and inference optimization rather than owning that yourself.
What You'll Bring:
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5+ Years of Experience in Machine Learning and Computer Vision, including models you personally carried from problem statement to measured outcome.
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A track record of framing problems. You can translate high-level product requirements into specific engineering tasks and explain technical trade-offs to non-expert stakeholders.
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Hands-on Experience Building Datasets at Scale: generation or collection, curation, automated quality assurance, coverage and bias analysis, and active learning to systematically close edge cases.
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Rigorous Evaluation Habits: baselines, ablations, metric design, and a clear sense of when a result is real.
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Expert-level Python and PyTorch skills.
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Comfortable with cloud training infrastructure (GCP or AWS), Docker, and experiment tracking tools.
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Professional experience with at least one of the following:
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Developing and training multi-task models
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Developing and training multi-modal models
-
Developing and training multi–view models
-
Self-supervised, outcome-based, or reward-driven learning.
Pay at Pickle
At Pickle Robot Company, we believe transparency builds trust. The salary range listed here is provided in accordance with Massachusetts law and reflects what we reasonably and in good faith expect to offer for this role. We often consider candidates at different levels of seniority, and final compensation will reflect the level at which a candidate is hired, along with factors like experience and location.
Pickle Perks
Pickle provides best-in-class benefits, including health, dental, and vision insurance; unlimited vacation; paid federal and state holidays; 401(k) contributions of 5% of your salary; company-covered travel expenses; and other perks to make your working life more fun, comfortable, and productive.

Source: Picklerobot careers (Lever)