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Engine Research Engineer Jobs (NOW HIRING)

Netic

Applied AI Research Engineer

San Francisco, CA · On-site

Netic is the AI revenue engine for essential services who are the backbone of the American economy ... As an Applied AI Research Engineer, you'll dive deep into cutting-edge research, understand the ...

Meta

Research Engineer, MRS AI

Bellevue, WA · On-site

$121K - $181K/yr

Meta is seeking a Research Engineer to join our Meta Recommendation Systems (MRS) AI Algorithm Team ... Join us to build Meta's User Intelligence Engine - a unified platform that models who the user is ...

ARA

Research Scientist/Engineer

Randolph, VT · On-site

$87K - $97K/yr

Familiarity with ROS2, Nix, Unreal Engine, CARLA, ProjectGL are advantageous. * Experience and ... Applied Research Associates, Inc. is an employee-owned international research and engineering ...

Sorion

Senior Research Engineer (Materials)

Houston, TX · On-site

$130K - $150K/yr

Role description As Senior Research Engineer, you will design and run hands-on experiments in ... Raman, SEM/EDS, TGA/DSC) * Develop intuition around solid-state transport, particle mechanics ...

University of Delaware

R&D Test Engineer

Newark, DE · On-site

R&D Test Engineer Job no: 502926 College / VP Area: Research Office Work type: Staff Location ... IC), SEM, Raman spectroscopy, and FTIR. * Operate and support research activities using spark ...

University of Delaware

R&D Test Engineer

Newark, DE · On-site

R&D Test Engineer Apply now Job no: 502926 College / VP Area: Research Office Work type: Staff ... IC), SEM, Raman spectroscopy, and FTIR. * Operate and support research activities using spark ...

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How much do engine research engineer jobs pay per year?

As of Jun 17, 2026, the average yearly pay for engine research engineer in the United States is $72,500.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,000.00 and $74,000.00 per year, depending on experience, location, and employer.

What is an Engine Research Engineer job?

An Engine Research Engineer is responsible for designing, analyzing, and improving engine performance, efficiency, and emissions. They conduct experiments, use simulation tools, and develop new technologies to enhance engine reliability and sustainability. Their work often involves collaboration with automotive, aerospace, or energy industries to optimize fuel consumption and reduce environmental impact.

What are the typical daily responsibilities of an Engine Research Engineer?

Engine Research Engineers routinely design and conduct experiments to test new engine concepts, analyze engine performance data, and develop improvements for efficiency and emissions. They often use simulation software to model engine behavior and interpret results to guide design decisions. Collaboration with other engineers, technicians, and sometimes external partners is a key part of the role, requiring regular meetings and technical discussions. These responsibilities support the ongoing development of cutting-edge engine technologies and help ensure compliance with industry and environmental regulations.

What are the key skills and qualifications needed to thrive in the Engine Research Engineer position, and why are they important?

To thrive as an Engine Research Engineer, you need a strong background in mechanical engineering, thermodynamics, and combustion processes, typically with at least a bachelor's degree in engineering. Familiarity with tools such as computational fluid dynamics (CFD) software, engine testing equipment, and data analysis platforms is essential, along with certifications like Six Sigma being a plus. Strong problem-solving skills, attention to detail, teamwork, and effective communication set top candidates apart. These skills are crucial for innovating and optimizing engine designs that meet performance, efficiency, and emissions standards within collaborative R&D environments.

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Engine Research Engineer jobs near you
Infographic showing various Engine Research Engineer job openings in the United States as of June 2026, with employment types broken down into 96% Full Time, and 4% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $72,500 per year, or $34.9 per hour.

Research Engineer, Multimodal Data

Eventual Computing

San Francisco, CA • On-site

$150K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 17 days ago

Job description

About Eventual
Every breakthrough Physical AI system - humanoid robots, autonomous vehicles, video generation models - is trained on petabytes of video, lidar, radar, and sensor data. But today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not the multimodal corpora that power AI. As a result, robotics and video-AI teams iterate on model improvement about once a week. Most of that week isn't training - it's finding the right data: writing CV heuristics over raw footage, paying annotators for edge cases, hand-curating clips before a cluster ever spins up. GPU bandwidth has grown 2-3× per generation. Storage and pipelines haven't. The gap widens every year.
Eventual was founded in 2022 to close it. Our open-source engine, Daft, is the distributed data engine purpose-built for multimodal AI - already running 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at Mobileye, TogetherAI, and CloudKitchens. We are building a video-native index on top of our engine for Physical AI that collapses the data iteration loop. Describe the dataset you want, get a curated table in minutes, feed it to your GPUs at line rate. One iteration per day becomes the norm.
We're building this in partnership with the top PhysicalAI labs and public AI infrastructure companies today. We have raised $30M from Felicis, CRV, Microsoft M12, Citi, Essence, Y Combinator, Caffeinated Capital, Array.vc, and angels from the co-founders of Databricks and Perplexity. We've assembled a world-class team from AWS, Render, Pinecone and Tesla. We have spent our careers powering the last generation of PhysicalAI in self-driving, and are excited to now do this for the next.
Join our small (but powerful!) team working together 4 days/week in our SF Mission district office.
Your Role
As a Research Engineer on the Visual Understanding team, you'll own the layer that makes petabytes of video queryable by content. Physical AI teams have raw footage, lidar, radar, and sim outputs scattered across object stores with no way to find what they need without weeks of human annotation. We change that economics: we run vision-language models over every clip in a corpus along axes the customer cares about (gripper type, failure mode, object class, scene, motion density), so a researcher can ask "left-arm grasp failures on deformable objects" and get a curated dataset in minutes.
You'll define the roadmap for our visual understanding capabilities, train and select the models that make corpus-scale annotation tractable at single-digit cents per hour of video, and build the rich datasets that go on to train customer models. This is a research engineering role - meaning you'll read papers and run experiments, but you ship to production and your work is judged by what it does for customer training runs.
Key Responsibilities
  • Own the visual understanding roadmap end-to-end: from picking the model family for a customer's taxonomy to landing it in production inference at corpus scale.
  • Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and convolutional perception models against customer datasets and benchmarks.
  • Drive down per-clip annotation cost - model selection, distillation, batching, decode pipelining - so "annotate every clip in a 10K-hour corpus" stays economical.
  • Build the rich, queryable datasets that customers train on: design taxonomies with researchers, instrument quality, version the outputs.
  • Partner with the dataloading and storage teams so visual understanding outputs flow into the index and on to the GPU without re-engineering.
  • Work directly with researchers at our partner labs - your shortest feedback loop is their next training iteration.

What we look for
  • Strong familiarity with modern vision and multimodal models - convolution nets, VLMs, VQA, embeddings - and a sense for the SOTA that's actually deployable today vs. on a leaderboard.
  • Experience running these models at scale on real video and sensor data, ideally for perception tasks (detection, tracking, segmentation, retrieval, captioning).
  • Background from a perception team at a self-driving, robotics, or visual-data company - or equivalent depth from a research lab.
  • Comfortable with cloud infrastructure and large-scale data processing - you don't need to be a distributed-systems engineer, but you've shipped jobs that ran on thousands of GPU-hours of video.
  • Bias toward data and infrastructure: you reach for "annotate the whole corpus" before "fine-tune another model."

Nice to have
  • Experience training vision or multimodal models from scratch (not just calling APIs).
  • ML/AI research background - papers, citations, or a research org on your resume.
  • Hands-on time with big-data frameworks like Spark, Ray, or Daft.
  • Worked on embeddings, retrieval, or content-aware search at scale.
  • Experience designing labeling taxonomies or running annotation programs.

Perks & Benefits
  • In-person, tight-knit team - 4 days/week in our SF Mission office.
  • Competitive comp and meaningful startup equity.
  • Catered lunches and dinners for SF employees.
  • Commuter benefit.
  • Team-building events and poker nights.
  • Health, vision, and dental coverage.
  • Flexible PTO.
  • Latest Apple equipment.
  • 401(k) plan with match.

If you're excited about being on the team that turns petabytes of raw video into the training data for the next generation of Physical AI, we'd love to talk.

Apply