Computer Vision Ml

Computer Vision Ml Engineer Jobs

2 jobs found

1d ago

Staff Machine Learning Engineer

Unity Technologies SF Mountain View, CA

Mountain View, CA

1d ago

$172k - $284k/yr Full time Ai Ml Ai Model Training Artificial Intelligence

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Job Description

Build production-grade AI for game experiences at Unity Technologies SF. This onsite role in Mountain View focuses on taking cutting-edge computer vision and multi-modal research into dependable, scalable systems across cloud, server, and on-device targets, with a team culture centered on measurement, engineering rigor, and cross-functional collaboration.

Responsibilities

Help set the technical vision and roadmap for computer vision and multi-modal AI models, including transformers, diffusion models, vision-language models, and JEPA-style generative architectures.
Design and implement models for image and video understanding and generation, including segmentation, detection, and dense prediction, plus multi-modal reasoning over images, text, and 3D inputs.
Make architecture and delivery decisions across quality, capability, latency, and cost for deployment targets, including training strategy, data pipelines, and evaluation.
Own the path from research prototype to production: training, fine-tuning, distillation, export, and serving, spanning cloud GPUs to efficient on-device inference where needed.
Collaborate closely with research scientists to translate novel CV and multi-modal architectures into deployable, well-engineered implementations.
Build scalable multi-modal inference systems that handle diverse inputs (images, video, text, primitives, and metadata) and generate outputs from semantic predictions to pixel-level generation.
Rapidly adopt field breakthroughs in areas such as vision-language pretraining and alignment, efficient diffusion (consistency models, flow matching), efficient attention (including FlashAttention and linear-attention variants), and tokenization or representation learning for vision.
When latency or device constraints require it, apply compression approaches such as quantization, pruning, and knowledge distillation, and work with runtimes including TensorRT, ONNX Runtime, CoreML, and TFLite.
Lead and mentor ML engineers, define engineering best practices and code review standards, and establish rigorous benchmarking and evaluation methodology.
Partner with research, platform engineers, product managers, and runtime teams to align ML capabilities with product roadmaps and target-platform constraints.
Champion a measurement culture by defining KPIs for model quality, accuracy, latency, memory, and cost, and ensuring consistent tracking.

Requirements

6+ years of ML engineering experience with substantial depth in computer vision and/or multi-modal modeling.
Production experience with transformer-based and diffusion-based vision models, including examples such as ViT, CLIP/SigLIP-style encoders, Stable Diffusion, and DETR/SAM-style architectures.
Strong command of the full model lifecycle, including data curation, training and fine-tuning, evaluation, and serving at scale.
Familiarity with efficient attention, diffusion samplers, multi-modal fusion, and vision-language alignment techniques.
Strong Python skills and modern deep-learning tooling (notably PyTorch), plus solid software engineering fundamentals.
Proven technical leadership, including setting direction, influencing cross-functional partners, and growing engineers.

Technologies

Python, PyTorch, TensorRT, ONNX Runtime, CoreML, TFLite, FlashAttention, ViT, CLIP, SigLIP, Stable Diffusion, DETR, SAM

Benefits

Comprehensive health, life, and disability insurance
Commute subsidy
Employee stock ownership
Competitive retirement/pension plans
Generous vacation and personal days
Support for new parents through leave and family-care programs
Office food snacks
Mental Health and Wellbeing programs and support
Employee Resource Groups
Global Employee Assistance Program
Training and development programs
Volunteering and donation matching program

Additional Information

Location: Mountain View, CA (onsite)
Salary: USD 172,200 - 283,900 per year
Zone A: $218,400 - $283,900; Zone B: $194,100 - $252,300; Zone C: $172,200 - $223,900
Beyond base salary, the role may be eligible for equity awards and participation in company incentive plans (such as annual discretionary bonuses or sales commissions)
Final offer amount depends on geographic location, relevant experience, professional background, and skill set

You Might Also Have

Experience with world-model, video-generation, or neural rendering pipelines (NeRF, 3DGS, or similar)
Experience deploying models to constrained or on-device targets, including quantization (INT8/INT4/FP16), pruning, distillation, and runtimes such as CoreML, TFLite, ONNX
Familiarity with mobile SoC accelerators (Apple Neural Engine, Qualcomm Hexagon/Adreno, ARM Mali) or compiler stacks such as MLIR, TVM, or XLA
Contributions to open-source ML frameworks or peer-reviewed CV/ML research publications
Background in real-time graphics or game engine pipelines (Metal, Vulkan, OpenGL ES)

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2d ago

Staff, Robotics ML/Data Engineer

Persona AI Inc Houston, TX

Houston, TX

2d ago

Full time Python Analytics Artificial Intelligence

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Job Description

Persona AI Inc is seeking a Staff-level Robotics ML/Data Engineer to architect and scale multimodal robotics data pipelines. The role focuses on transforming raw, in-the-wild egocentric video and dense sensor streams into high-fidelity training assets that directly support foundation model development.

Responsibilities

Design cross-modal validation systems that verify agreement between video, proprioception, force/haptic signals, and language annotations. Examples include reprojecting robot state into the image plane to confirm video-state consistency, and using VLM-assisted checks to assess whether instructions align with observed behavior.
Orchestrate hand-tracking, segmentation, depth estimation, 3D reconstruction, and pose-tracking components. Retarget human demonstrations into robot trajectories, and run simulation-in-the-loop validation using kinematic feasibility, physics replay, and motion-consistency filtering to ensure synthesized data is physically grounded rather than only visually plausible.
Implement robust data augmentation methods to expand expert trajectories and improve learning robustness, including spatial transformations, temporal scaling, synthetic viewpoints, and sensor noise injection.
Create unified state-action representations across different embodiments, coordinate frames, rotation conventions, gripper and hand parameterizations, and sampling rates. Apply per-dimension validity masking and per-source normalization so that onboarding a new robot or sensor becomes a configuration update instead of a code rewrite.
Build dataset tooling that enables researchers to query, visualize, and audit data, including clip browsers, trajectory viewers, and annotation review user interfaces. Translate model-failure analysis into new curation rules and targeted re-collection requests.
Architect end-to-end ingestion pipelines that convert raw, unstructured sources into indexed, queryable, training-ready datasets. Include temporal segmentation of long recordings into action clips, metadata and scene-graph extraction, embedding-based retrieval, and language annotation workflows.

Requirements

M.S. or Ph.D. in Computer Science, Data Engineering, Machine Learning, Robotics, or a related field.
Deep expertise in Python with extensive experience using PyTorch, especially for custom dataloaders for multimodal datasets.
Experience analyzing and processing complex time-series data from force-torque sensors, load cells, or tactile arrays, with careful alignment to visual frames.
Strong knowledge of video processing pipelines and libraries including OpenCV, FFmpeg, and Decord, including managing I/O bottlenecks for terabyte-scale video datasets.
Solid robotics and 3D geometry knowledge covering coordinate frames and transforms, rotation representations, camera intrinsics and extrinsics, forward/inverse kinematics, and URDF.
Proven ability to implement programmatic and generative data augmentation for computer vision and time-series data.

Technologies

Python, PyTorch, OpenCV, FFmpeg, Decord, URDF, Ray, Apache Spark, Open X-Embodiment, DROID, AgiBot World, EgoDex, SAM-family, MANO, SMPL, Omniverse, MuJoCo, NVIDIA robotic software stack, NVIDIA's robotic software stack, VLM

Benefits

Competitive compensation
Performance-based bonus
99% employer covered medical benefits
Early-stage equity
Competitive PTO
Company-wide paid winter break between December 24th and January 2nd
Full access to advanced tools

Bonus Skills

Experience with NVIDIA’s robotic software stack (Open X-Embodiment, DROID, AgiBot World, EgoDex, or similar).
Comfort using modern perception tools as a user, including segmentation (SAM-family), monocular depth, hand/body pose estimation (MANO/SMPL), and 6-DoF object pose tracking and point tracking. Experience integrating and evaluating these components in a pipeline is expected.
Familiarity with distributed data processing systems such as Ray and Apache Spark.
Background in generating or utilizing synthetic robotic data through simulation using Omniverse and MuJoCo.
Experience integrating spatial awareness or tactile data representations (for example, Fourier encoding) into visual pipelines.

Job Details

Department: Software
Reports To: Teleoperations Lead
Employment Type: Full-Time
Location: Houston, TX (onsite); Houston, TX or Pensacola, FL

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Computer Vision Ml Engineer Jobs

Staff Machine Learning Engineer

Staff, Robotics ML/Data Engineer

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