Robotics Engineer – SLAM
Pittsburgh, Pennsylvania / $140000 - $180000
$140000 - $180000
Robotics Engineer - SLAM
$140,000 - $180,000
Do you have a strong background in AI/Autonomous systems, SLAM, and mobile robotics? Keep reading to learn more about a new opportunity with an Industrial Robotics Start-Up.
This innovative company is looking for a Robotics Engineer that can hit the ground running on AMR projects. They're fresh off a funding round and are looking to make waves in the industry with their product.
The Robotics Perception Engineer must have extensive experience with LiDAR and SLAM. Python and C++ are required and familiarity with Cartographer is a major plus (ROS is also helpful). The ideal candidate is highly experienced with computer vision/perception, mobile robotics, autonomous vehicles, GPS-denied environments, and difficult-to-navigate settings.
The team has a fantastic culture and is looking for someone that genuinely loves start-up environments and can hit the ground running in a perception role focused on AMRs. Experience with production of a robotics product from start to finish will be very useful in this role.
YOUR SKILLS AND EXPERIENCE
- 3+ years of experience in AI, autonomy, and robotics/perception engineering
- A thorough understanding about robotics, perception, AMRs, and SLAM
- Ability to produce solutions independently, communicate effectively, and be able to work in or with a team
- Experience deploying an open-source SLAM software package
- Experience writing code that targets Jetson SOM by NVIDIA
- Ability to work in-person in a Pittsburgh-based office
Computer vision, AMR, deep learning, AI, Autonomy, lead engineer, robotics, computer science, artificial intelligence, SLAM, Python, industrial robotics, autonomous vehicles, research, robotics, R&D, development, localization, mapping, C++, ROS, Pytorch, LiDAR
Data Engineer Or Software Engineer: What Does Your Business Need? | Harnham US Recruitment post
We are in a time in which what we do with Data matters. Over the last few years, we have seen a rapid rise in the number of Data Scientists and Machine Learning Engineers as businesses look to find deeper insights and improve their strategies. But, without proper access to the right Data that has been processed and massaged, Data Scientists and Machine Learning Engineers would be unable to do their job properly. So who are the people who work in the background and are responsible to make sure all of this works? The quick answer is Data Engineers!… or is it? In reality, there are two similar, yet different profiles who can help help a company achieve their Data-driven goals. Data Engineers When people think of Data Engineers, they think of people who make Data more accessible to others within an organization. Their responsibility is to make sure the end user of the Data, whether it be an Analyst, Data Scientist, or an executive, can get accurate Data from which the business can make insightful decisions. They are experts when it comes to data modeling, often working with SQL. Frequently, “modern” Data Engineers work with a number of tools including Spark, Kafka, and AWS (or any cloud provider), whilst some newer Databases/Data Warehouses include Mongo DB and Snowflake. Companies are choosing to leverage these technologies and update their stack because it allows Data teams to move at a much faster pace and be able to deliver results to their stakeholders. An enterprise looking for a Data Engineer will need someone to focus more on their Data Warehouse and utilize their strong knowledge of querying information, whilst constantly working to ingest/process Data. Data Engineers also focus more on Data Flow and knowing how each Data sets works in collaboration with one another. Software Engineers – DataSimilar to a Data Engineers, Software Engineers – Data ( who I will refer to as Software Data Engineers in this article) also build out Data Pipelines. These individuals might go by different names like Platform or Infrastructure Engineer. They have to be good with SQL and Data Modeling, working with similar technologies such as Spark, AWS, and Hadoop. What separates Software Data Engineers from Data Engineers is the necessity to look at things from a macro-level. They are responsible for building out the cluster manager and scheduler, the distributed cluster system, and implementing code to make things function faster and more efficiently. Software Data Engineers are also better programers. Frequently, they will work in Python, Java, Scala, and more recently, Golang. They also work with DevOps tools such as Docker, Kubernetes, or some sort of CI/CD tool like Jenkins. These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient. This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model. Which is right for your business? If you are looking for someone who can focus extensively on pulling Data from a Data source or API, before transforming or “massaging” the Data, and then moving it elsewhere, then you are looking for a Data Engineer. Quality Data Engineers will be really good at querying Data and Data Modeling and will also be good at working with Data Warehouses and using visualization tools like Tableau or Looker. If you need someone who can wear multiple hats and build highly scalable and distributed systems, you are looking for a Software Data Engineer. It’s more common to see this role in smaller companies and teams, since Hiring Managers often need someone who can do multiple tasks due to budget constraints and the need for a leaner team. They will also be better coders and have some experience working with DevOps tools. Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse. It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled.That’s not to say a Data Engineer can’t work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual’s past experience and the projects they have worked on, rather than looking at their previous job titles. If you’re looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help. Take a look at our latest opportunities or get in touch if you have any questions.
Is There An Increased Demand for SLAM Engineers? | Harnham US Recruitment post
Ever wondered how your Roomba knows not to vacuum the same area twice? Enter SLAM Engineering. No visions of tennis, basketball, or family breakfasts here, but this SLAM is an acronym and stands for simultaneous localization and mapping. This is the algorithmic technology that drives autonomous vehicles, AR, and robots.SLAM (simultaneous localization and mapping) is the technology your Roomba uses to vacuum your space. Algorithms map out the unknown area and then that information is carried out by engineers who develop systems for path planning and teach it to avoid obstacles. But it’s more than what drives your Roomba. It can also drive robots and autonomous vehicles,
so what’s driving the increase in demand?
Four Key Factors Contributing to Growth According to the SLAM Technology Market Research Report, the market is expected to increase forty-two percent between 2021 and 2030 up from $157.5 million in 2021. To reach this goal, there are four key factors contributing to this growth:The emergence of autonomous vehicles.Increased AR applications.Increased use and demand of unmanned aerial vehicles (UAVs).Advancements in visual SLAM.Connected vehicles technology is helping improve safety and efficiency which is increasing interest from the automotive industry, tech companies, the military, and the general population. UAVs, location mapping, surveillance, and detection are set to make the military the fastest growing category by end user. The sensors, cameras, and algorithms also lend themselves to the ability to increase safety for soldiers and keep humans from hazardous conditions. Whether it’s UAVs, autonomous vehicles, AR applications, or field robots what is it that makes SLAM work?
How SLAM Works
We dreamed of flying cars before we had the technology, and while we’re not yet jetting off in our Kia to skies unknown, there have been significant advancements in computer processing speed and more cost-efficient cameras and sensors to drive us into the future. To achieve simultaneous localization and mapping (SLAM), there are two types of technology required.Sensor signal processing Pose-graph optimizationWhen it comes to Computer Vision and how SLAM Engineers and engineering is in demand, there are two other definitions to consider. The first is visual SLAM (vSLAM) and the second is Monocular SLAM.Visual SLAM (or vSLAM) uses simple camera images using such as wide angle, spherical, multi-camera sensors, and is an integral piece when it comes to embedded vision. Monocular SLAM is when vSLAM uses a single camera as the only sensor to measure physical variables such as velocity and orientation. Autonomous vehicles, UAVs, and connected vehicle technologies are just the beginning for SLAM engineering, but every industry is on the lookout for top talent to help them improve and refine their localization and mapping dynamics. This is a new and emerging field and demand is high. If you’ve ever wanted to get in on the ground floor of something new and find out what you’re capable of, SLAM Engineering may be for you.If you’re a business on the cutting edge in the automotive, manufacturing, or aerial fields, and want to get ahead of your competition, now is the time to find out what a SLAM Engineer can do for your business.If you’re interested in Digital Analytics, Computer Vision, Advanced Analytics, Data Science, Machine Learning, or Robotics just to name a few, Harnham may have a role for you.
Check out our latest Computer Vision jobs or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Arizona Team, contact us at (602) 562 7011 or send an email to email@example.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to firstname.lastname@example.org.
Keepers of the Data Kingdom: the Analytics Engineer | Harnham US Recruitment post
If it seems the Data world is drilling down further into niche specialities, you’re right. Considering the swathes of information sent and received on a day-by-day, minute-by-minute, and second-by-second basis, is it any wonder? The sheer volume, depending on your business and what you want to know, requires not just a Data team, but must now include someone with a particular skillset, including the tech-savvy analyst who can speak to the executive team.So, who holds it all together? These swathes of information. Who organizes the information in a cohesive order, so anyone with a map, can make their own analyses? Enter the Analytics Engineer.What Makes an Analytics Engineer an Analytics Engineer?Though it’s a rather new speciality within the Data Scientist scope—think Machine Learning Engineer, Software Engineer, Business Analyst, etc—at its core, the definition of an Analytics Engineer is this: “The analytics engineer sits at the intersection of the skill sets of data scientists, analysts, and data engineers. They bring a formal and rigorous software engineering practice to the efforts of analysts and data scientists, and they bring an analytical and business-outcomes mindset to the efforts of data engineering.” Michael Kaminsky, consultant, and former Director of Analytics at Harry’s.In other words, analytics engineers, using best software engineering practices transform data through testing and documentation so that data analysts begin with cleaner data to analyze. As technically savvy as the engineer must be, they must also be able to explain to stakeholders what they’re looking at so they can formulate their own insights. Five Roles and Responsibilities of the Analytics EngineerLike all new niche specialities, there are core responsibilities to consider as well as that of skillsets required to either study to become an Analytics Engineer or to discover if you’re one already. How? Consider the questions you ask, your studies within Data Science, Computer Science, Statistics, and Math, and your balance between technical skills and soft skills. Below are five things to consider when thinking about this role:Programming language experience. Experience with programming languages like R and Python along with strong SQL skills which are at the core of this role. DBT technology knowledge. As the driving force behind the rise of Analytics Engineer as a separate role, it’s imperative anyone interested in pursuing it should have a firm grasp of DBT — the Data Build Tool — that allows the implementation of analytics code using SQL. Data tracking expertise using Git. Data modelling. Clean, tested, and raw data which allow executives and analysts to view their Data, understand it within the database or its warehouse. Data transformation. Analytics Engineers determine what Data is most useful and transform it to ensure it fits related tasks. It’s part of building the foundational layer so businesses can answer their own questions. Key Changes Leading to the Shift in Data RolesWith every technological advancement their comes new players to the field. The difference is here is that the job description already existed. We were only missing a title. But from the traditional Data team to the modern Data team, there are a few key changes that point directly to the rise of this niche field. Cloud warehouses (like Snowflake, Redshift, BigQuery) and the arrival of the DBT the foundational layer which can be built on top of modern data warehouses are the first two that come to mind. Then, the Software-as-a-Service (SaaS) tools like Stitch and Hevo are capable of integrating Data from a variety of sources, and the introduction of tools like Mode and Looker allows anyone interested in drawing insight from Data to do so on their own.Who Needs an Analytics Engineer? Small or Large Businesses?The short answer is it depends. But the general rule follows that while both large and small companies can benefit from having this professional on their staff, there are different things to consider. For example, a small business may be able to find what they need in a single individual. The Analytics Engineer is something of a jack-of-all-trades. Larger businesses, on the other hand, may already have a Data team in place. In this case, an Analytics Engineer adds to your team, something like an additional set of eyes increasing insight drawn from those large swathes of Data we spoke about earlier.So, what’s next for the role of Analytics Engineer? Who knows? The roles of any Data industry professional is constantly evolving. If you’re interested in Analytics Engineering, Machine Learning, Data Science, or Business Intelligence just to name a few, Harnham may have a role for you. Check out our latest Data & Analytics Engineering jobs or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 – 4999 or send an email to email@example.com. For our Arizona Team, contact us at (602) 562 7011 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
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